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Preprint

  • VisualBERT: A Simple and Performant Baseline for Vision and Language

    Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang, in Arxiv, 2019.
    Full Text Code Abstract BibTeX Details
    We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.
    @inproceedings{li2019visualbert,
      author = {Li, Liunian Harold and Yatskar, Mark and Yin, Da and Hsieh, Cho-Jui and Chang, Kai-Wei},
      title = {VisualBERT: A Simple and Performant Baseline for Vision and Language},
      booktitle = {Arxiv},
      year = {2019}
    }
    
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2019

  • Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization

    Ching-pei Lee and Kai-Wei Chang, in Machine Learning Journal, 2019.
    Full Text Abstract BibTeX Details
    Designing distributed algorithms for empirical risk minimization (ERM) has become an active research topic in recent years because of the practical need to deal with the huge volume of data. In this paper, we propose a general framework for training an ERM model via solving its dual problem in parallel over multiple machines. Our method provides a versatile approach for many large-scale machine learning problems, including linear binary/multi-class classification, regression, and structured prediction. Comparing with existing approaches, we show that our method has faster convergence under weaker conditions both theoretically and empirically.
    @inproceedings{LD17,
      author = {Lee, Ching-pei and Chang, Kai-Wei},
      title = {Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization},
      booktitle = {Machine Learning Journal},
      year = {2019}
    }
    
    Details
  • Learning to Represent Bilingual Dictionaries

    Muhao Chen, Yingtao Tian, Haochen Chen, Kai-Wei Chang, Steve Skiena, and Carlo Zaniolo, in CoNLL, 2019.
    Full Text Abstract BibTeX Details
    Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages.
    However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications
    such as cross-lingual semantic search and textual inference.
    To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries.
    The proposed model is trained to map the lexical definitions to the cross-lingual target words,
    for which we explore with different sentence encoding techniques.
    To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model.
    We conduct experiments on two new tasks.
    In the cross-lingual reverse dictionary retrieval task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and the proposed learning strategies are effective.
    In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.
    
    @inproceedings{chen2019leanring,
      author = {Chen, Muhao and Tian, Yingtao and Chen, Haochen and Chang, Kai-Wei and Skiena, Steve and Zaniolo, Carlo},
      title = { Learning to Represent Bilingual Dictionaries},
      booktitle = {CoNLL},
      year = {2019}
    }
    
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  • Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages

    Wasi Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, and Nanyun Peng, in CoNLL, 2019.
    Full Text Code Abstract BibTeX Details
    Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. 
    One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings.
    In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations 
    Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer.
    We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. 
    Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. 
    We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training. 
    
    @inproceedings{ahmad2019crosslingual,
      author = {Ahmad, Wasi and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
      title = {  Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
      booktitle = {CoNLL},
      year = {2019}
    }
    
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  • Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

    Tao Meng, Nanyun Peng, and Kai-Wei Chang, in EMNLP, 2019.
    Full Text Code Abstract BibTeX Details
    Prior work on cross-lingual dependency pars-ing often focuses on capturing the commonal-ities between source and target languages andoverlook the potential to leverage the linguis-tic properties of the target languages to fa-cilitate the transfer. In this paper, we showthat weak supervisions of linguistic knowl-edge for the target languages can improve across-lingual graph-based dependency parsersubstantially. Specifically, we explore severaltypes ofcorpus linguistic statisticsand com-pile them intocorpus-statistics constraintstofacilitate the inference procedure. We proposenew algorithms that adapt two techniques,Lagrangian relaxation and posterior regular-ization, to conduct inference with corpus-statistics constraints. Experiments show thatthe Lagrangian relaxation and posterior reg-ularization techniques improve the perfor-mances on 15 and 17 out of 19 target lan-guages, respectively. The improvements areespecially large for the target languages thathave different word order features from thesource language.
    @inproceedings{meng2019target,
      author = {Meng, Tao and Peng, Nanyun and Chang, Kai-Wei},
      title = {Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing},
      booktitle = {EMNLP},
      year = {2019}
    }
    
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  • Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification

    Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang, and Wei Wang, in EMNLP, 2019.
    Full Text Abstract BibTeX Details
    Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel framework, learning to DIScriminate Perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models. To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations. For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search. DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure. Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification. In addition, in-depth analysis shows the robustness of DISP across different situations.
    @inproceedings{zhou2019learning,
      author = {Zhou, Yichao and Jiang, Jyun-Yu and Chang, Kai-Wei and Wang, Wei},
      title = {Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification},
      booktitle = {EMNLP},
      year = {2019}
    }
    
    Details
  • Examining Gender Bias in Languages with Grammatical Gender

    Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell, and Kai-Wei Chang, in EMNLP, 2019.
    Full Text Code Abstract BibTeX Details
    Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embeddings under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches effectively reduce the gender bias while preserving the utility of the embeddings.
    @inproceedings{zhou2019examining,
      author = {Zhou, Pei and Shi, Weijia and Zhao, Jieyu and Huang, Kuan-Hao and Chen, Muhao and Cotterell, Ryan and Chang, Kai-Wei},
      title = {Examining Gender Bias in Languages with Grammatical Gender},
      booktitle = {EMNLP},
      year = {2019}
    }
    
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  • Robust Text Classifier on Test-Time Budgets

    Md Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, and Venkatesh Saligrama, in EMNLP (short), 2019.
    Full Text Code Abstract BibTeX Details
    We propose a generic and interpretable learning framework for building robust text classification model that achieves accuracy comparable to full models under test-time budget constraints. Our approach learns a selector to identify words that are relevant to the prediction tasks and passes them to the classifier for processing. The selector is trained jointly with the classifier and directly learns to incorporate with the classifier. We further propose a data aggregation scheme to improve the robustness of the classifier. Our learning framework is general and can be incorporated with any type of text classification model. On real-world data, we show that the proposed approach improves the performance of a given classifier and speeds up the model with a mere loss in accuracy performance.
    @inproceedings{parvez2019robust,
      author = {Parvez, Md Rizwan and Bolukbasi, Tolga and Chang, Kai-Wei and Saligrama, Venkatesh},
      title = {Robust Text Classifier on Test-Time Budgets},
      booktitle = {EMNLP (short)},
      year = {2019}
    }
    
    Details
  • The Woman Worked as a Babysitter: On Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP (short), 2019.
    Full Text Code BibTeX Details
    @inproceedings{sheng2019woman,
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      title = {The Woman Worked as a Babysitter: On Biases in Language Generation},
      booktitle = {EMNLP (short)},
      abstract_url = {We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.},
      year = {2019}
    }
    
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  • Retrofitting Contextualized Word Embeddings with Paraphrases

    Weijia Shi, Muhao Chen, Pei Zhou, and Kai-Wei Chang, in EMNLP (short), 2019.
    Full Text BibTeX Details
    @inproceedings{shi2019retrofitting,
      author = {Shi, Weijia and Chen, Muhao and Zhou, Pei and Chang, Kai-Wei},
      title = {Retrofitting Contextualized Word Embeddings with Paraphrases},
      booktitle = {EMNLP (short)},
      year = {2019}
    }
    
    Details
  • Visualizing Trend of Key Roles in News Articles

    Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, and Kai-Wei Chang, in EMNLP (demo), 2019.
    Full Text Video Code BibTeX Details
    @inproceedings{xia2019visualizing,
      author = {Xia, Chen and Zhang, Haoxiang and Moghtader, Jacob and Wu, Allen and Chang, Kai-Wei},
      title = {Visualizing Trend of Key Roles in News Articles},
      booktitle = {EMNLP (demo)},
      year = {2019}
    }
    
    Details
  • Efficient Contextual Representation Learning With Continuous Outputs

    Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, and Kai-Wei Chang, in TACL, 2019.
    Full Text Abstract BibTeX Details
    Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.
    @inproceedings{LCHC19,
      author = {Li, Liunian Harold and Chen, Patrick H. and Hsieh, Cho-Jui and Chang, Kai-Wei},
      title = {Efficient Contextual Representation Learning With Continuous Outputs},
      booktitle = {TACL},
      year = {2019}
    }
    
    Details
  • Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

    Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez, in ICCV, 2019.
    Full Text Abstract BibTeX Details
    In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables –such as gender– in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network – and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.
    @inproceedings{wang2019balanced,
      author = {Wang, Tianlu and Zhao, Jieyu and Yatskar, Mark and Chang, Kai-Wei and Ordonez, Vicente},
      title = {Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations},
      booktitle = {ICCV},
      year = {2019}
    }
    
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  • Few-Shot Representation Learning for Out-Of-Vocabulary Words

    Ziniu Hu, Ting Chen, Kai-Wei Chang, and Yizhou Sun, in ACL, 2019.
    Full Text Poster Abstract BibTeX Details
    Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. It is challenging to learn accurate representations of these words with only a few observations. In this paper, we formulate the learning of OOV embeddings as a few-shot regression problem, and address it by training a representation function to predict the oracle embedding vector (defined as embedding trained with abundant observations) based on limited observations. Specifically, we propose a novel hierarchical attention-based architecture to serve as the neural regression function, with which the context information of a word is encoded and aggregated from K observations. Furthermore, our approach can leverage Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing accurate embeddings for OOV words, and improves downstream tasks where these embeddings are utilized.
    @inproceedings{hu2019fewshot,
      author = {Hu, Ziniu and Chen, Ting and Chang, Kai-Wei and Sun, Yizhou},
      title = {Few-Shot Representation Learning for Out-Of-Vocabulary Words},
      booktitle = {ACL},
      year = {2019}
    }
    
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  • Debiasing Gender in Natural Language Processing: Literature Review

    Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Kai-Wei Chang, and William Yang Wang, in ACL, 2019.
    Full Text Slides Abstract BibTeX Details
    As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.
    @inproceedings{sun2019debiasing,
      author = {Sun, Tony and Gaut, Andrew and Tang, Shirlyn and Huang, Yuxin and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Chang, Kai-Wei and Wang, William Yang},
      title = {Debiasing Gender in Natural Language Processing: Literature Review},
      booktitle = {ACL},
      slides_url = {/documents/slides/sun2019debiasing_slide.pdf},
      year = {2019}
    }
    
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  • On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing

    Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng, in NAACL, 2019.
    Full Text Video Code Abstract BibTeX Details
    Different languages might have different wordorders. In this paper, we investigate cross-lingual transfer and posit that an order-agnostic model will perform better when trans-ferring to distant foreign languages. To test ourhypothesis, we train dependency parsers on anEnglish corpus and evaluate their transfer per-formance on 30 other languages. Specifically,we compare encoders and decoders based onRecurrent Neural Networks (RNNs) and mod-ified self-attentive architectures. The formerrelies on sequential information while the lat-ter is more flexible at modeling word order.Rigorous experiments and detailed analysisshows that RNN-based architectures transferwell to languages that are close to English,while self-attentive models have better overallcross-lingual transferability and perform espe-cially well on distant languages.
    @inproceedings{ahmad2019difficulties,
      author = {Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Hovy, Eduard and Chang, Kai-Wei and Peng, Nanyun},
      title = {On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing},
      booktitle = {NAACL},
      year = {2019}
    }
    
    Details
  • Gender Bias in Contextualized Word Embeddings

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2019.
    Full Text Slides Video Abstract BibTeX Details
    Despite the great success of contextualized word embeddings on downstream applications, these representations potentially embed the societal biases exhibited in their training corpus. In this paper, we quantify, analyze and mitigate the gender bias exhibited in ELMo contextualized word vectors. We first demonstrate that the vectors encode and propagate information about genders unequally and then conduct a principal component analysis to visualize the geometry of the gender information in the embeddings. Then we show that ELMo works unequally well for men and women in down-stream tasks. Finally, we explore a variety of methods to remove such gender bias and demonstrate that it can be reduced through data augmentation.
    @inproceedings{zhao2019gender,
      author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Cotterell, Ryan and Ordonez, Vicente and Chang, Kai-Wei},
      title = {Gender Bias in Contextualized Word Embeddings},
      booktitle = {NAACL (short)},
      slides_url = {/documents/slides/zhao2019gender_slide.pdf},
      year = {2019}
    }
    
    Details
  • Context Attentive Document Ranking and Query Suggestion

    Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in SIGIR, 2019.
    Full Text Slides Code Abstract BibTeX Details
    We present a context-aware neural ranking model to exploit users’ on-task search activities and enhance retrieval performance. Inparticular, a two-level hierarchical recurrent neural network isintroduced to learn search context representation of individualqueries, search tasks, and corresponding dependency structure byjointly optimizing two companion retrieval tasks: document rank-ing and query suggestion. To identify variable dependency structurebetween search context and users’ ongoing search activities, at-tention at both levels of recurrent states are introduced. Extensiveexperiment comparisons against a rich set of baseline methods andan in-depth ablation analysis confirm the value of our proposedapproach for modeling search context buried in search tasks.
    @inproceedings{ahmad2019context,
      author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning},
      title = {Context Attentive Document Ranking and Query Suggestion},
      booktitle = {SIGIR},
      slides_url = {/documents/slides/ahmad2019context_slide.pdf},
      year = {2019}
    }
    
    Details
  • Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN

    Muhao Chen, Chelsea J.-T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang, in ISMB, 2019.
    Full Text Abstract BibTeX Details
    Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information. Hence, we present an end-to-end framework, Lasagna, for PPI predictions using only the primary sequences of a protein pair. Lasagna incorporates a deep residual recurrent convolutional neural network in the Siamese learning architecture, which leverages both robust local features and contextualized information that are significant for capturing the mutual influence of protein sequences. Our framework relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that Lasagna outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.
    @inproceedings{CJZCZCZW19,
      author = {Chen, Muhao and Ju, Chelsea J.-T. and Zhou, Guangyu and Chen, Xuelu and Zhang, Tianran and Chang, Kai-Wei and Zaniolo, Carlo and Wang, Wei},
      title = {Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN},
      booktitle = {ISMB},
      year = {2019}
    }
    
    Details
  • Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

    Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun, in ICLR 2019 Workshop: Representation Learning on Graphs and Manifolds, 2019.
    Full Text Abstract BibTeX Details
    Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of labeled data and expressive features, which might be inaccessible for some applications. To tackle this problem, we propose a pre-training framework that captures generic graph structural information that is transferable across tasks. Our framework can leverage the following three tasks: 1) denoising link reconstruction, 2) centrality score ranking, and 3) cluster preserving. The pre-training procedure can be conducted purely on the synthetic graphs, and the pre-trained GNN is then adapted for downstream applications. With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks. Comprehensive experiments demonstrate that our proposed framework can significantly enhance the performance of various tasks at the level of node, link, and graph.
    @inproceedings{HFCCS19,
      author = {Hu, Ziniu and Fan, Changjun and Chen, Ting and Chang, Kai-Wei and Sun, Yizhou},
      title = {Pre-Training Graph Neural Networks for Generic Structural Feature Extraction},
      booktitle = {ICLR 2019 Workshop: Representation Learning on Graphs and Manifolds},
      year = {2019}
    }
    
    Details
  • Learning Bilingual Word Embeddings Using Lexical Definitions

    Weijia Shi, Muhao Chen, Yingtao Tian, and Kai-Wei Chang, in Repl4NLP (ACL workshop), 2019.
    Full Text Abstract BibTeX Details
    Bilingual word embeddings, which represent lexicons of different languages in a shared embedding space, are essential for supporting semantic and knowledge transfers in a variety of cross-lingual NLP tasks. Existing approaches to training bilingual word embeddings require either large collections of pre-defined seed lexicons that are expensive to obtain, or parallel sentences that comprise coarse and noisy alignment. In contrast, we propose BiLex that leverages publicly available lexical definitions for bilingual word embedding learning. Without the need of predefined seed lexicons, BiLex comprises a novel word pairing strategy to automatically identify and propagate the precise fine-grain word alignment from lexical definitions. We evaluate BiLex in word-level and sentence-level translation tasks, which seek to find the cross-lingual counterparts of words and sentences respectively. BiLex significantly outperforms previous embedding methods on both tasks.
    @inproceedings{shi2019bilingual,
      author = {Shi, Weijia and Chen, Muhao and Tian, Yingtao and Chang, Kai-Wei},
      title = {Learning Bilingual Word Embeddings Using Lexical Definitions},
      booktitle = {Repl4NLP (ACL workshop)},
      poster = {/documents/slides/shi2019bilingual_poster.pdf},
      year = {2019}
    }
    
    Details

2018

  • Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions

    Dat Duong, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, and Jingyi Jessica Li, in Journal of Computational Biology, 2018.
    Full Text Code Abstract BibTeX Details
    The Gene Ontology (GO) database contains GO terms that describe biological functions of genes.
    Previous methods for comparing GO terms have relied on the fact that GO terms are organized
    into a tree structure. Under this paradigm, the locations of two GO terms in the tree dictate their
    similarity score. In this paper, we introduce two new solutions for this problem, by focusing
    instead on the definitions of the GO terms. We apply neural network based techniques from
    the natural language processing (NLP) domain. The first method does not rely on the GO tree,
    whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO
    definitions by treating them as two unordered sets of words. The word similarity is estimated by a
    word embedding model that maps words into an N-dimensional space. In our second approach,
    we account for the word-ordering within a sentence. We use a sentence encoder to embed GO
    definitions into vectors and estimate how likely one definition entails another. We validate our
    methods in two ways. In the first experiment, we test the model’s ability to differentiate a true
    protein-protein network from a randomly generated network. In the second experiment, we test
    the model in identifying orthologs from randomly-matched genes in human, mouse, and fly. In
    both experiments, a hybrid of NLP and GO-tree based method achieves the best classification
    accuracy.
    @inproceedings{DAECL18,
      author = {Duong, Dat and Ahmad, Wasi Uddin and Eskin, Eleazar and Chang, Kai-Wei and Li, Jingyi Jessica},
      title = {Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions},
      booktitle = {Journal of Computational Biology},
      year = {2018}
    }
    
    Details
  • Generating Natural Language Adversarial Examples

    Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang, in EMNLP (short), 2018.
    Full Text Abstract BibTeX Details
    Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the network to misclassify. In the image domain, these perturbations can often be made virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a population-based optimization algorithm to generate semantically and syntactically similar adversarial examples. We demonstrate via a human study that 94.3% of the generated examples are classified to the original label by human evaluators, and that the examples are perceptibly quite similar. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.
    @inproceedings{alzanto2018generating,
      author = {Alzantot, Moustafa and Sharma, Yash and Elgohary, Ahmed and Ho, Bo-Jhang and Srivastava, Mani and Chang, Kai-Wei},
      title = {Generating Natural Language Adversarial Examples},
      booktitle = {EMNLP (short)},
      year = {2018}
    }
    
    Details
  • Learning Gender-Neutral Word Embeddings

    Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang, in EMNLP (short), 2018.
    Full Text Code Abstract BibTeX Details
    Word embeddings have become a fundamental component in a wide range of Natu-ral Language Processing (NLP) applications.However, these word embeddings trained onhuman-generated corpora inherit strong gen-der stereotypes that reflect social constructs.In this paper, we propose a novel word em-bedding model, De-GloVe, that preserves gen-der information in certain dimensions of wordvectors while compelling other dimensions tobe free of gender influence. Quantitative andqualitative experiments demonstrate that De-GloVe successfully isolates gender informa-tion without sacrificing the functionality of theembedding model.
    @inproceedings{zhao2018learning,
      author = {Zhao, Jieyu and Zhou, Yichao and Li, Zeyu and Wang, Wei and Chang, Kai-Wei},
      title = {Learning Gender-Neutral Word Embeddings},
      booktitle = {EMNLP (short)},
      year = {2018}
    }
    
    Details
  • Building Language Models for Text with Named Entities

    Md Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in ACL, 2018.
    Full Text Poster Code Abstract BibTeX Details
    Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 40.3% on code generation than state-of-the-art language models.
    @inproceedings{parvez2018building,
      author = {Parvez, Md Rizwan and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei},
      title = {Building Language Models for Text with Named Entities},
      booktitle = {ACL},
      year = {2018}
    }
    
    Details
  • Learning Word Embeddings for Low-resource Languages by PU Learning

    Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, and Kai-Wei Chang, in NAACL, 2018.
    Full Text Slides Video Code Abstract BibTeX Details
    Word embedding has been used as a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is very sparse because many word pairs are not observed to co-occur. In contrast to existing approaches, we argue that the zero entries in the co-occurrence matrix also provide valuable information and design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix. The experimental results demonstrate that the proposed approach requires a smaller amount of training text to obtain a reasonable word embedding model.
    @inproceedings{jiang2018learning,
      author = {Jiang, Chao and Yu, Hsiang-Fu and Hsieh, Cho-Jui and Chang, Kai-Wei},
      title = {Learning Word Embeddings for Low-resource Languages by PU Learning},
      booktitle = {NAACL},
      slides_url = {/documents/slides/jiang2018learning_slide.pdf},
      year = {2018}
    }
    
    Details
  • Gender Bias in Coreference Resolution:Evaluation and Debiasing Methods

    in NAACL (short), 2018.
    Full Text Poster Code Abstract BibTeX Details
    In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.
    @inproceedings{zhao2018gender,
      author = {},
      title = {Gender Bias in Coreference Resolution:Evaluation and Debiasing Methods},
      booktitle = {NAACL (short)},
      press_url = {https://www.stitcher.com/podcast/matt-gardner/nlp-highlights/e/55861936},
      year = {2018}
    }
    
    Details
  • Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

    Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, and Carlo Zaniolo, in IJCAI, 2018.
    Full Text Abstract BibTeX Details
    Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge enabled with cross-lingual inferences that benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.
    @inproceedings{chen2018multilingual,
      author = {Chen, Muhao and Tian, Yingtao and Chang, Kai-Wei and Skiena, Steven and Zaniolo, Carlo},
      title = {Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment},
      booktitle = {IJCAI},
      year = {2018}
    }
    
    Details
  • Multi-Task Learning for Document Ranking and Query Suggestion

    Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in ICLR, 2018.
    Full Text Code Abstract BibTeX Details
    We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommen tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
    @inproceedings{ahmad2018multitask,
      author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning},
      title = {Multi-Task Learning for Document Ranking and Query Suggestion},
      booktitle = {ICLR},
      year = {2018}
    }
    
    Details
  • Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search

    Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in SIGIR, 2018.
    Full Text Code Abstract BibTeX Details
    Modern web search engines exploit users’ search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk of privacy infringement and raises serious public concerns. In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario. In our solution, each user query is submitted with l additional cover queries and corresponding clicks, which act as decoys to mask users’ genuine search intent from a search engine. The cover queries are sequentially sampled from a set of hierarchically organized language models to ensure the coherency of fake search intents in a cover search task. Our approach emphasizes the plausibility of generated cover queries, not only to the current genuine query but also to previous queries in the same task, to increase the complexity for a search engine to identify a user’s true intent. We also develop two new metrics from an information theoretic perspective to evaluate the effectiveness of provided privacy protection. Comprehensive experiment comparisons with state-of-the-art query obfuscation techniques are performed on the public AOL search log, and the propitious results substantiate the effectiveness of our solution.
    @inproceedings{ahmad2018intent,
      author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning},
      title = {Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search},
      booktitle = {SIGIR},
      year = {2018}
    }
    
    Details
  • Counterexamples for Robotic Planning Explained in Structured Language

    Lu Feng, Mahsa Ghasemi, Kai-Wei Chang, and Ufuk Topcu, in ICRA, 2018.
    Full Text Abstract BibTeX Details
    Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations. However, such artifacts may be too complex for humans to understand, because existing representations of counterexamples typically include a large number of paths or a complex automaton. To help improve the interpretability of counterexamples, we define a notion of explainable counterexample, which includes a set of structured natural language sentences to describe the robotic behavior that lead to a requirement violation in an MDP model of robotic mission plan. We propose an approach based on mixed-integer linear programming for generating explainable counterexamples that are minimal, sound and complete. We demonstrate the usefulness of the proposed approach via a case study of warehouse robots planning.
    @inproceedings{feng2018conterexamples,
      author = {Feng, Lu and Ghasemi, Mahsa and Chang, Kai-Wei and Topcu, Ufuk},
      title = {Counterexamples for Robotic Planning Explained in Structured Language},
      booktitle = {ICRA},
      year = {2018}
    }
    
    Details
  • A Corpus to Learn Refer-to-as Relations for Nominals

    Wasi Ahmad and Kai-Wei Chang, in LREC, 2018.
    Full Text Code Abstract BibTeX Details
    Continuous representations for words or phrases, trained on large unlabeled corpora are proved very useful for many natural language processing tasks. While these vector representations capture many fine-grained syntactic and semantic regularities among words or phrases, it often lacks coreferential information which is useful for many downstream tasks like information extraction, text summarization etc. In this paper, we argue that good word and phrase embeddings should contain information for identifying refer-to-as relationship and construct a corpus from Wikipedia to generate coreferential neural embeddings for nominals. The term \emphnominal refers to a word or a group of words that functions like a noun phrase. In addition, we use coreference resolution as a proxy to evaluate the learned neural embeddings for noun phrases. To simplify the evaluation procedure, we design a coreferential phrase prediction task where the learned nominal embeddings are used to predict which candidate nominals can be referred to a target nominal. We further describe how to construct an evaluation dataset for such task from well known OntoNotes corpus and demonstrate encouraging baseline results.
    @inproceedings{AC18,
      author = {Ahmad, Wasi and Chang, Kai-Wei},
      title = {A Corpus to Learn Refer-to-as Relations for Nominals},
      booktitle = {LREC},
      year = {2018}
    }
    
    Details
  • A Corpus of Drug Usage Guidelines Annotated with Type of Advice

    Sarah Masud Preum, Md. Rizwan Parvez, Kai-Wei Chang, and John Stankovic, in LREC, 2018.
    Full Text Abstract BibTeX Details
    Adherence to drug usage guidelines for prescription and over-the-counter drugs is critical for drug safety and effectiveness of treatment. Drug usage guideline documents contain advice on potential drug-drug interaction, drug-food interaction, and drug administration process. Current research on drug safety and public health indicates patients are often either unaware of such critical advice or overlook them. Categorizing advice statements from these documents according to their topics can enable the patients to find safety critical information. However, automatically categorizing drug usage guidelines based on their topic is an open challenge and there is no annotated dataset on drug usage guidelines. To address the latter issue, this paper presents (i) an annotation scheme for annotating safety critical advice from drug usage guidelines, (ii) an annotation tool for such data, and (iii) an annotated dataset containing drug usage guidelines from 90 drugs. This work is expected to accelerate further release of annotated drug usage guideline datasets and research on automatically filtering safety critical information from these textual documents.
    @inproceedings{PPCS18,
      author = {Preum, Sarah Masud and Parvez, Md. Rizwan and Chang, Kai-Wei and Stankovic, John},
      title = {A Corpus of Drug Usage Guidelines Annotated with Type of Advice},
      booktitle = {LREC},
      year = {2018}
    }
    
    Details
  • Quantification and Analysis of Scientific Language Variation Across Research Fields

    Pei Zhou, Muhao Chen, Kai-Wei Chang, and Carlo Zaniolo, in CDEC (workshop at ICDM), 2018.
    Full Text Abstract BibTeX Details
    Quantifying differences in terminologies from various academic domains has been a longstanding problem yet to be
    solved. We propose a computational approach for analyzing linguistic variation among scientific research fields by capturing the
    semantic change of terms based on a neural language model. The
    model is trained on a large collection of literature in five computer
    science research fields, for which we obtain field-specific vector
    representations for key terms, and global vector representations
    for other words. Several quantitative approaches are introduced
    to identify the terms whose semantics have drastically changed,
    or remain unchanged across different research fields. We also
    propose a metric to quantify the overall linguistic variation of
    research fields. After quantitative evaluation on human annotated
    data and qualitative comparison with other methods, we show
    that our model can improve cross-disciplinary data collaboration
    by identifying terms that potentially induce confusion during
    interdisciplinary studies.
    @inproceedings{ZCCZ18,
      author = {Zhou, Pei and Chen, Muhao and Chang, Kai-Wei and Zaniolo, Carlo},
      title = {Quantification and Analysis of Scientific Language Variation Across Research Fields},
      booktitle = {CDEC (workshop at ICDM)},
      year = {2018}
    }
    
    Details

2017

  • Counterfactual Language Model Adaptation for Suggesting Phrases

    Kenneth Arnold, Kai-Wei Chang, and Adam T. Kalai, in IJCNLP (short), 2017.
    Full Text Abstract BibTeX Details
    We study the challenge of suggesting multi-word phrases to be inserted while typing on a mobile keyboard. Recent work in mobile text entry user-interfaces has shown that, unlike single-word predictions, these phrases are treated as suggestions rather than predictions, meaning that users often insert words that weren’t what they were planning on typing.
    This suggests the NLP problem of offering multi-word suggestions that are likely to be accepted by a user. We propose a method for customizing an existing language model to adapt it to a specific such task, and show how to learn the parameters of that customization offline.
    @inproceedings{ACK17,
      author = {Arnold, Kenneth and Chang, Kai-Wei and Kalai, Adam T.},
      title = {Counterfactual Language Model Adaptation for Suggesting Phrases},
      booktitle = {IJCNLP (short)},
      year = {2017}
    }
    
    Details
  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in EMNLP, 2017.
    Full Text Slides Code Abstract BibTeX Details EMNLP 2017 Best Long Paper Award
    Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occuring labels and visual input but risk inadvertently encoding social biases found in web corpora.
    In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, but a trained model amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for the resulting inference problems. Our method results in no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 33.3% and 44.9% for multilabel classification and visual semantic role labeling, respectively.
    @inproceedings{zhao2017men,
      author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
      title = {Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints},
      booktitle = {EMNLP},
      slides_url = {documents/slides/zhao2017men_slide.pdf},
      year = {2017}
    }
    
    Details
  • Structured Prediction with Test-time Budget Constraints

    Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, and Venkatesh Saligrama, in AAAI, 2017.
    Full Text Abstract BibTeX Details
    We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two real-world structured prediction tasks, optical character recognition (OCR) and dependency parsing. For OCR our method cuts the feature acquisition time by half coming within a 1% margin of top accuracy. For dependency parsing we realize an overall runtime gain of 20% without significant loss in performance.
    @inproceedings{bolukbasi2017structured,
      author = {Bolukbasi, Tolga and Chang, Kai-Wei and Wang, Joseph and Saligrama, Venkatesh},
      title = {Structured Prediction with Test-time Budget Constraints},
      booktitle = {AAAI},
      slide_url = {documents/slides/bolukbasi2017structured_slide.pdf},
      year = {2017}
    }
    
    Details
  • Beyond Bilingual: Multi-senseWord Embeddings using Multilingual Context

    Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, and James Zou, in ACL RepL4NLP Workshop, 2017.
    Full Text Abstract BibTeX Details Best Paper Award
    Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.
    @inproceedings{upadhyay2017beyond,
      author = {Upadhyay, Shyam and Chang, Kai-Wei and Taddy, Matt and Kalai, Adam and Zou, James},
      title = {Beyond Bilingual: Multi-senseWord Embeddings using Multilingual Context},
      booktitle = {ACL RepL4NLP Workshop},
      year = {2017}
    }
    
    Details

2016

  • EMNLP 16 Workshop on Structured Prediction for NLP

    Kai-Wei Chang, Ming-Wei Chang, Vivek Srikumar, and Alexander M. Rush, in EMNLP, 2016.
    Full Text Abstract BibTeX Details
    Many prediction tasks in NLP involve assigning values to mutually dependent variables. For example, when designing a model to automatically perform linguistic analysis of a sentence or a document (e.g., parsing, semantic role labeling, or discourse analysis), it is crucial to model the correlations between labels. Many other NLP tasks, such as machine translation, textual entailment, and information extraction, can be also modeled as structured prediction problems.
    In order to tackle such problems, various structured prediction approaches have been proposed, and their effectiveness has been demonstrated. Studying structured prediction is interesting from both NLP and machine learning (ML) perspectives. From the NLP perspective, syntax and semantics of natural language are clearly structured and advances in this area will enable researchers to understand the linguistic structure of data. From the ML perspective, the large amount of available text data and complex linguistic structures bring challenges to the learning community. Designing expressive yet tractable models and studying efficient learning and inference algorithms become important issues.
    Recently, there has been significant interest in non-standard structured prediction approaches that take advantage of non-linearity, latent components, and/or approximate inference in both the NLP and ML communities. Researchers have also been discussing the intersection between deep learning and structured prediction through the DeepStructure reading group. This workshop intends to bring together NLP and ML researchers working on diverse aspects of structured prediction and expose the participants to recent progress in this area.
    Workshop Site
    @inproceedings{CCSR16,
      author = {Chang, Kai-Wei and Chang, Ming-Wei and Srikumar, Vivek and Rush, Alexander M.},
      title = {EMNLP 16 Workshop on Structured Prediction for NLP},
      booktitle = {EMNLP},
      year = {2016}
    }
    
    Details
  • Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

    Shyam Upadhyay, Ming-Wei Chang, Kai-Wei Chang, and Wen-tau Yih, in EMNLP, 2016.
    Full Text Abstract BibTeX Details
    Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG-514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.
    Dataset
    @inproceedings{BCWS16,
      author = {Upadhyay, Shyam and Chang, Ming-Wei and Chang, Kai-Wei and Yih, Wen-tau},
      title = {Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems},
      booktitle = {EMNLP},
      year = {2016}
    }
    
    Details
  • Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

    Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai, in NeurIPS, 2016.
    Full Text Code Abstract BibTeX Details reported by NPR and MIT Tech Review
    The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.
    @inproceedings{bolukbasi2016man,
      author = {Bolukbasi, Tolga and Chang, Kai-Wei and Zou, James and Saligrama, Venkatesh and Kalai, Adam},
      title = {Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings},
      booktitle = {NeurIPS},
      year = {2016}
    }
    
    Details
  • A Credit Assignment Compiler for Joint Prediction

    Kai-Wei Chang, He He, Hal Daume III, John Langford, and Stephane Ross, in NeurIPS, 2016.
    Full Text Code Abstract BibTeX Details
    Many machine learning applications involve jointly predicting multiple mutually dependent output variables. Learning to search is a family of methods where the complex decision problem is cast into a sequence of decisions via a search space. Although these methods have shown promise both in theory and in practice, implementing them has been burdensomely awkward. In this paper, we show the search space can be defined by an arbitrary imperative program, turning learning to search into a credit assignment compiler. Altogether with the algorithmic improvements for the compiler, we radically reduce the complexity of programming and the running time. We demonstrate the feasibility of our approach on multiple joint prediction tasks. In all cases, we obtain accuracies as high as alternative approaches, at drastically reduced execution and programming time.
    @inproceedings{chang2016credit,
      author = {Chang, Kai-Wei and He, He and III, Hal Daume and Langford, John and Ross, Stephane},
      title = {A Credit Assignment Compiler for Joint Prediction},
      booktitle = {NeurIPS},
      year = {2016}
    }
    
    Details

2015

  • Learning to Search for Dependencies

    Kai-Wei Chang, He He, Hal Daume; III, and John Lanford, in Arxiv, 2015.
    Full Text Code Abstract BibTeX Details
    We demonstrate that a dependency parser can be built using a credit assignment compiler which removes the burden of worrying about low-level machine learning details from the parser implementation. The result is a simple parser which robustly applies to many languages that provides similar statistical and computational performance with best-to-date transition-based parsing approaches, while avoiding various downsides including randomization, extra feature requirements, and custom learning algorithms.
    @inproceedings{chang2015learning,
      author = {Chang, Kai-Wei and He, He and III, Hal Daume; and Lanford, John},
      title = {Learning to Search for Dependencies},
      booktitle = {Arxiv},
      year = {2015}
    }
    
    Details
  • IllinoisSL: A JAVA Library for Structured Prediction

    Kai-Wei Chang, Shyam Upadhyay, Ming-Wei Chang, Vivek Srikumar, and Dan Roth, in Arxiv, 2015.
    Full Text Abstract BibTeX Details
    Training a structured prediction model involves performing several loss-augmented inference steps. Over the lifetime of the training, many of these inference problems, although different, share the same solution. We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the training process by exploiting this redundancy of solutions, without compromising the performance of the model. We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entity-relation extraction task. Our method learns the same model as an exact training algorithm would, but call the inference engine only in 10% . 24% of the inference problems encountered during training. We observe similar gains on a multi-label classification task and with a Structured Perceptron model for the entity-relation task.
    @inproceedings{chang2015illinoissl,
      author = {Chang, Kai-Wei and Upadhyay, Shyam and Chang, Ming-Wei and Srikumar, Vivek and Roth, Dan},
      title = {IllinoisSL: A JAVA Library for Structured Prediction},
      booktitle = {Arxiv},
      year = {2015}
    }
    
    Details
  • Distributed Training of Structured SVM

    Ching-pei Lee, Kai-Wei Chang, Shyam Upadhyay, and Dan Roth, in OPT workshop at NeurIPS, 2015.
    Full Text Abstract BibTeX Details
    Training structured prediction models is time-consuming. However, most existing approaches only use a single machine, thus, the advantage of computing power and the capacity for larger data sets of multiple machines have not been exploited. In this work, we propose an efficient algorithm for distributedly training structured support vector machines based on a distributed block-coordinate descent method. Both theoretical and experimental results indicate that our method is efficient.
    @inproceedings{lee2015distributed,
      author = {Lee, Ching-pei and Chang, Kai-Wei and Upadhyay, Shyam and Roth, Dan},
      title = {Distributed Training of Structured SVM},
      booktitle = {OPT workshop at NeurIPS},
      year = {2015}
    }
    
    Details
  • Learning to Search Better Than Your Teacher

    Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume; III, and John Langford, in ICML, 2015.
    Full Text Code Abstract BibTeX Details
    Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor?
    We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.
    @inproceedings{CKADL15,
      author = {Chang, Kai-Wei and Krishnamurthy, Akshay and Agarwal, Alekh and III, Hal Daume; and Langford, John},
      title = {Learning to Search Better Than Your Teacher},
      booktitle = {ICML},
      year = {2015}
    }
    
    Details
  • A Joint Framework for Coreference Resolution and Mention Head Detection

    Haoruo Peng, Kai-Wei Chang, and Dan Roth, in CoNLL, 2015.
    Full Text Abstract BibTeX Details
    In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred mentions. However, there are significant gaps between the performance on gold mentions and the performance on the real problem, when mentions are predicted from raw text via an imperfect Mention Detection (MD) module. Motivated by the goal of reducing such gaps, we develop an ILP-based joint coreference resolution and mention head formulation that is shown to yield significant improvements on coreference from raw text, outperforming existing state-of-art systems on both the ACE-2004 and the CoNLL-2012 datasets. At the same time, our joint approach is shown to improve mention detection by close to 15% F1. One key insight underlying our approach is that identifying and co-referring mention heads is not only sufficient but is more robust than working with complete mentions.
    @inproceedings{PengChRo15,
      author = {Peng, Haoruo and Chang, Kai-Wei and Roth, Dan},
      title = {A Joint Framework for Coreference Resolution and Mention Head Detection},
      booktitle = {CoNLL},
      year = {2015}
    }
    
    Details
  • Structural Learning with Amortized Inference

    Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, and Dan Roth, in AAAI, 2015.
    Full Text Poster Abstract BibTeX Details
    Training a structured prediction model involves performing several loss-augmented inference steps. Over the lifetime of the training, many of these inference problems, although different, share the same solution. We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the training process by exploiting this redundancy of solutions, without compromising the performance of the model. We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entity-relation extraction task. Our method learns the same model as an exact training algorithm would, but call the inference engine only in 10% . 24% of the inference problems encountered during training. We observe similar gains on a multi-label classification task and with a Structured Perceptron model for the entity-relation task.
    @inproceedings{chang2015structural,
      author = {Chang, Kai-Wei and Upadhyay, Shyam and Kundu, Gourab and Roth, Dan},
      title = {Structural Learning with Amortized Inference},
      booktitle = {AAAI},
      year = {2015}
    }
    
    Details
  • Selective Algorithms for Large-Scale Classification and Structured Learning

    Kai-Wei Chang, in UIUC Phd Thesis, 2015.
    Full Text Abstract BibTeX Details
    The desired output in many machine learning tasks is a structured object, such as tree, clustering, or sequence. Learning accurate prediction models for such problems requires training on large amounts of data, making use of expressive features and performing global inference that simultaneously assigns values to all interrelated nodes in the structure. All these contribute to significant scalability problems. In this thesis, we describe a collection of results that address several aspects of these problems - by carefully selecting and caching samples, structures, or latent items.
    Our results lead to entryfficient learning algorithms for large-scale binary classification models, structured prediction models and for online clustering models which, in turn, support reduction in problem size, improvements in training and evaluation speed and improved performance. We have used our algorithms to learn expressive models from large amounts of annotated data and achieve state-of-the art performance on several natural language processing tasks.
    @inproceedings{chang2015thesis,
      author = {Chang, Kai-Wei},
      title = {Selective Algorithms for Large-Scale Classification and Structured Learning},
      booktitle = {UIUC Phd Thesis},
      year = {2015}
    }
    
    Details

2014

  • A Discriminative Latent Variable Model for Online Clustering

    Rajhans Samdani, Kai-Wei Chang, and Dan Roth, in ICML, 2014.
    Full Text Abstract BibTeX Details
    This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, L3M outperforms several existing online as well as batch supervised clustering techniques.
    @inproceedings{samdani2014discriminative,
      author = {Samdani, Rajhans and Chang, Kai-Wei and Roth, Dan},
      title = {A Discriminative Latent Variable Model for Online Clustering},
      booktitle = {ICML},
      slide_url = {/documents/slides/samdani2014discriminative_slide.pdf},
      year = {2014}
    }
    
    Details
  • Typed Tensor Decomposition of Knowledge Bases for Relation Extraction

    Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Chris Meek, in EMNLP, 2014.
    Full Text Video Abstract BibTeX Details
    While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowledge bases and textual data, the performance of relation extraction can be improved significantly. Following this new paradigm, we propose a tensor decomposition approach for knowledge base embedding that is highly scalable, and is especially suitable for relation extraction. By leveraging relational domain knowledge about entity type information, our learning algorithm is significantly faster than previous approaches and is better able to discover new relations missing from the database. In addition, when applied to a relation extraction task, our approach alone is comparable to several existing systems, and improves the weighted mean average precision of a state-of-the-art method by 10 points when used as a subcomponent.
    @inproceedings{chang2014typed,
      author = {Chang, Kai-Wei and Yih, Wen-tau and Yang, Bishan and Meek, Chris},
      title = {Typed Tensor Decomposition of Knowledge Bases for Relation Extraction},
      booktitle = {EMNLP},
      year = {2014}
    }
    
    Details
  • The Illinois-Columbia System in the CoNLL-2014 Shared Task

    Alla Rozovskaya, Kai-Wei Chang, Mark Sammons, Dan Roth, and Nizar Habash, in CoNLL Shared Task, 2014.
    Full Text Abstract BibTeX Details
    The CoNLL-2014 shared task is an extension of last year’s shared task and focuses on correcting grammatical errors in essays written by non-native learners of English. In this paper, we describe the Illinois-Columbia system that participated in the shared task. Our system ranked second on the original annotations and first on the revised annotations.
    The core of the system is based on the University of Illinois model that placed first in the CoNLL-2013 shared task. This baseline model has been improved and expanded for this year’s competition in several respects. We describe our underlying approach, which relates to our previous work, and describe the novel aspects of the system in more detail.
    @inproceedings{RCSRH14,
      author = {Rozovskaya, Alla and Chang, Kai-Wei and Sammons, Mark and Roth, Dan and Habash, Nizar},
      title = {The Illinois-Columbia System in the CoNLL-2014 Shared Task},
      booktitle = {CoNLL Shared Task},
      year = {2014}
    }
    
    Details

2013

  • A Constrained Latent Variable Model for Coreference Resolution

    Kai-Wei Chang, Rajhans Samdani, and Dan Roth, in EMNLP, 2013.
    Full Text Poster Abstract BibTeX Details
    Coreference resolution is a well known clustering task in Natural Language Processing. In this paper, we describe the Latent Left Linking model (L3M), a novel, principled, and linguistically motivated latent structured prediction approach to coreference resolution.
    We show that L3M admits efficient inference and can be augmented with knowledge-based constraints; we also present a fast stochastic gradient based learning.
    Experiments on ACE and Ontonotes data show that L3M and its constrained version, CL3M, are more accurate than several state-of-the-art approaches as well as some structured prediction models proposed in the literature.
    @inproceedings{ChangSaRo13,
      author = {Chang, Kai-Wei and Samdani, Rajhans and Roth, Dan},
      title = {A Constrained Latent Variable Model for Coreference Resolution},
      booktitle = {EMNLP},
      year = {2013}
    }
    
    Details
  • Multi-Relational Latent Semantic Analysis

    Kai-Wei Chang, Wen-tau Yih, and Chris Meek, in EMNLP, 2013.
    Full Text Slides Abstract BibTeX Details
    We present Multi-Relational Latent Semantic Analysis (MRLSA) which generalizes Latent Semantic Analysis (LSA). MRLSA provides an elegant approach to combining multiple relations between words by constructing a 3-way tensor. Similar to LSA, a low-rank approximation of the tensor is derived using a tensor decomposition. Each word in the vocabulary is thus represented by a vector in the latent semantic space and each relation is captured by a latent square matrix. The degree of two words having a specific relation can then be measured through simple linear algebraic operations. We demonstrate that by integrating multiple relations from both homogeneous and heterogeneous information sources, MRLSA achieves state-of-the-art performance on existing benchmark datasets for two relations, antonymy and is-a.
    @inproceedings{chang2013mrlsa,
      author = {Chang, Kai-Wei and Yih, Wen-tau and Meek, Chris},
      title = {Multi-Relational Latent Semantic Analysis},
      booktitle = {EMNLP},
      slides_url = {/documents/slides/chang2013mrlsa_slide.pdf},
      year = {2013}
    }
    
    Details
  • Multi-core Structural SVM Training

    Kai-Wei Chang, Vivek Srikumar, and Dan Roth, in ECML, 2013.
    Poster Abstract BibTeX Details
    Many problems in natural language processing and computer vision can be framed as structured prediction problems. Structural support vector machines (SVM) is a popular approach for training structured predictors, where learning is framed as an optimization problem. Most structural SVM solvers alternate between a model update phase and an inference phase (which predicts structures for all training examples). As structures become more complex, inference becomes a bottleneck and thus slows down learning considerably. In this paper, we propose a new learning algorithm for structural SVMs called DEMI-DCD that extends the dual coordinate descent approach by decoupling the model update and inference phases into different threads. We take advantage of multi-core hardware to parallelize learning with minimal synchronization between the model update and the inference phases. We prove that our algorithm not only converges but also fully utilizes all available processors to speed up learning, and validate our approach on two real-world NLP problems: part-of-speech tagging and relation extraction. In both cases, we show that our algorithm utilizes all available processors to speed up learning and achieves competitive performance. For example, it achieves a relative duality gap of 1% on a POS tagging problem in 192 seconds using 16 threads, while a standard implementation of a multi-threaded dual coordinate descent algorithm with the same number of threads requires more than 600 seconds to reach a solution of the same quality.
    @inproceedings{chang2013multicore,
      author = {Chang, Kai-Wei and Srikumar, Vivek and Roth, Dan},
      title = {Multi-core Structural SVM Training},
      booktitle = {ECML},
      year = {2013}
    }
    
    Details
  • Tractable Semi-Supervised Learning of Complex Structured Prediction Models

    Kai-wei Chang, S. Sundararajan, and S. Sathiya Keerthi, in ECML, 2013.
    Full Text Slides Poster Abstract BibTeX Details
    Semi-supervised learning has been widely studied in the literature. However, most previous works assume that the output structure is simple enough to allow the direct use of tractable inference/learning algorithms (e.g., binary label or linear chain). Therefore, these methods cannot be applied to problems with complex structure. In this paper, we propose an approximate semi-supervised learning method that uses piecewise training for estimating the model weights and a dual decomposition approach for solving the inference problem of finding the labels of unlabeled data subject to domain specific constraints. This allows us to extend semi-supervised learning to general structured prediction problems. As an example, we apply this approach to the problem of multi-label classification (a fully connected pairwise Markov random field). Experimental results on benchmark data show that, in spite of using approximations, the approach is effective and yields good improvements in generalization performance over the plain supervised method. In addition, we demonstrate that our inference engine can be applied to other semi-supervised learning frameworks, and extends them to solve problems with complex structure.
    @inproceedings{ChangSuKe13,
      author = {Chang, Kai-wei and Sundararajan, S. and Keerthi, S. Sathiya},
      title = {Tractable Semi-Supervised Learning of Complex Structured Prediction Models},
      booktitle = {ECML},
      slides_url = {../slides/ChangSuKe13_slide.pdf},
      year = {2013}
    }
    
    Details
  • The University of Illinois System in the CoNLL-2013 Shared Task

    Alla Rozovskaya, Kai-Wei Chang, Mark Sammons, and Dan Roth, in CoNLL Shared Task, 2013.
    Full Text Poster Abstract BibTeX Details
    The CoNLL-2013 shared task focuses on correcting grammatical errors in essays written by non-native learners of English. In this paper, we describe the University of Illinois system that participated in the shared task. The system consists of five components and targets five types of common grammatical mistakes made by English as Second Language writers. We describe our underlying approach, which relates to our previous work, and describe the novel aspects of the system in more detail. Out of 17 participating teams, our system is ranked first based on both the original annotation and on the revised annotation.
    @inproceedings{RCSR13,
      author = {Rozovskaya, Alla and Chang, Kai-Wei and Sammons, Mark and Roth, Dan},
      title = {The University of Illinois System in the CoNLL-2013 Shared Task},
      booktitle = {CoNLL Shared Task},
      year = {2013}
    }
    
    Details

2012

  • Efficient Pattern-Based Time Series Classification on GPU

    Kai-Wei Chang, Baplab Deka, W.-M. W. Hwu, and Dan Roth, in ICDM, 2012.
    Full Text Abstract BibTeX Details
    Time series shapelet discovery algorithm finds subsequences from a set of time series for use as primitives for time series classification. This algorithm has drawn a lot of interest because of the interpretability of its results. However, computation requirements restrict the algorithm from dealing with large data sets and may limit its application in many domains. In this paper, we address this issue by redesigning the algorithm for implementation on highly parallel Graphics Process Units (GPUs). We investigate several concepts of GPU programming and propose a dynamic programming algorithm that is suitable for implementation on GPUs. Results show that the proposed GPU implementation significantly reduces the running time of the shapelet discovery algorithm. For example, on the largest sample dataset from the original authors, the running time is reduced from half a day to two minutes.
    @inproceedings{CDHR12,
      author = {Chang, Kai-Wei and Deka, Baplab and Hwu, W.-M. W. and Roth, Dan},
      title = {Efficient Pattern-Based Time Series Classification on GPU },
      booktitle = {ICDM},
      year = {2012}
    }
    
    Details
  • Illinois-Coref: The UI System in the CoNLL-2012 Shared Task

    Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Mark Sammons, and Dan Roth, in CoNLL Shared Task, 2012.
    Full Text Poster Abstract BibTeX Details
    The CoNLL-2012 shared task is an extension of the last year��s coreference task. We participated in the closed track of the shared tasks in both years. In this paper, we present the improvements of Illinois-Coref system from last year. We focus on improving mention detection and pronoun coreference resolution, and present a new learning protocol. These new strategies boost the performance of the system by 5% MUC F1, 0.8% BCUB F1, and 1.7% CEAF F1 on the OntoNotes-5.0 development set.
    @inproceedings{CSRSR12,
      author = {Chang, Kai-Wei and Samdani, Rajhans and Rozovskaya, Alla and Sammons, Mark and Roth, Dan},
      title = {Illinois-Coref: The UI System in the CoNLL-2012 Shared Task},
      booktitle = {CoNLL Shared Task},
      year = {2012}
    }
    
    Details
  • Large Linear Classification When Data Cannot Fit In Memory

    Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin, in TKDD, 2012.
    Full Text Code Abstract BibTeX Details Best Paper Award, KDD 10
    Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. As data cannot fit in memory, many design considerations are very different from those for traditional algorithms. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method.
    @inproceedings{yu2010large,
      author = {Yu, Hsiang-Fu and Hsieh, Cho-Jui and Chang, Kai-Wei and Lin, Chih-Jen},
      title = {Large Linear Classification When Data Cannot Fit In Memory},
      booktitle = {TKDD},
      year = {2012}
    }
    
    Details

2011

  • Selective Block Minimization for Faster Convergence of Limited Memory Large-scale Linear Models

    Kai-Wei Chang and Dan Roth, in KDD, 2011.
    Full Text Slides Poster Code Abstract BibTeX Details
    As the size of data sets used to build classifiers steadily increases, training a linear model efficiently with limited memory becomes essential. Several techniques deal with this problem by loading blocks of data from disk one at a time, but usually take a considerable number of iterations to converge to a reasonable model. Even the best block minimization techniques [1] require many block loads since they treat all training examples uniformly. As disk I/O is expensive, reducing the amount of disk access can dramatically decrease the training time.
    @inproceedings{ChangRo11,
      author = {Chang, Kai-Wei and Roth, Dan},
      title = {Selective Block Minimization for Faster Convergence of Limited Memory Large-scale Linear Models},
      booktitle = {KDD},
      slides_url = {http://cogcomp.cs.illinois.edu/files/presentations/kdd_slide.pdf},
      year = {2011}
    }
    
    Details
  • Inference Protocols for Coreference Resolution

    Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Nick Rizzolo, Mark Sammons, and Dan Roth, in CoNLL Shared Task, 2011.
    Full Text Slides Poster Abstract BibTeX Details
    This paper presents Illinois-Coref, a system for coreference resolution that participated in the CoNLL-2011 shared task. We investigate two inference methods, Best-Link and All-Link, along with their corresponding, pairwise and structured, learning protocols. Within these, we provide a flexible architecture for incorporating linguistically-motivated constraints, several of which we developed and integrated. We compare and evaluate the inference approaches and the contribution of constraints, analyze the mistakes of the system, and discuss the challenges of resolving coreference for the OntoNotes-4.0 data set.
    @inproceedings{CSRRSR11,
      author = {Chang, Kai-Wei and Samdani, Rajhans and Rozovskaya, Alla and Rizzolo, Nick and Sammons, Mark and Roth, Dan},
      title = {Inference Protocols for Coreference Resolution},
      booktitle = {CoNLL Shared Task},
      slides_url = {http://cogcomp.cs.illinois.edu/files/presentations/Inference%20Protocols%20for%20Coreference%20Resolution.pptx},
      year = {2011}
    }
    
    Details

2010

  • Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

    Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin, in JMLR, 2010.
    Full Text Code Abstract BibTeX Details
    Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is often time consuming. In contrast, we can efficiently train and test much larger data sets using linear SVM without kernels. In this work, we apply fast linear-SVM methods to the explicit form of polynomially mapped data and investigate implementation issues. The approach enjoys fast training and testing, but may sometimes achieve accuracy close to that of using highly nonlinear kernels. Empirical experiments show that the proposed method is useful for certain large-scale data sets. We successfully apply the proposed method to a natural language processing (NLP) application by improving the testing accuracy under some training/testing speed requirements.
    @inproceedings{CHCRL10,
      author = {Chang, Yin-Wen and Hsieh, Cho-Jui and Chang, Kai-Wei and Ringgaard, Michael and Lin, Chih-Jen},
      title = {Training and Testing Low-degree Polynomial Data Mappings via Linear SVM},
      booktitle = {JMLR},
      year = {2010}
    }
    
    Details
  • Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

    Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin, in JMLR, 2010.
    Full Text Abstract BibTeX Details
    Maximum entropy (Maxent) is useful in natural language processing and many other areas. Iterative scaling (IS) methods are one of the most popular approaches to solve Maxent. With many variants of IS methods, it is difficult to understand them and see the differences. In this paper, we create a general and unified framework for iterative scaling methods. This framework also connects iterative scaling and coordinate descent methods. We prove general convergence results for IS methods and analyze their computational complexity. Based on the proposed framework, we extend a coordinate descent method for linear SVM to Maxent. Results show that it is faster than existing iterative scaling methods.
    @inproceedings{HHCL10,
      author = {Huang, Fang-Lan and Hsieh, Cho-Jui and Chang, Kai-Wei and Lin, Chih-Jen},
      title = {Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models},
      booktitle = {JMLR},
      year = {2010}
    }
    
    Details
  • A Comparison of Optimization Methods and software for Large-scale L1-regularized Linear Classification

    Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin, in JMLR, 2010.
    Full Text Code Abstract BibTeX Details
    Large-scale linear classification is widely used in many areas. The L1-regularized form can be applied for feature selection; however, its non-differentiability causes more difficulties in training. Although various optimization methods have been proposed in recent years, these have not yet been compared suitably. In this paper, we first broadly review existing methods. Then, we discuss state-of-the-art software packages in detail and propose two efficient implementations. Extensive comparisons indicate that carefully implemented coordinate descent methods are very suitable for training large document data.
    @inproceedings{YCHL10,
      author = {Yuan, Guo-Xun and Chang, Kai-Wei and Hsieh, Cho-Jui and Lin, Chih-Jen},
      title = {A Comparison of Optimization Methods and software for Large-scale L1-regularized Linear Classification},
      booktitle = {JMLR},
      year = {2010}
    }
    
    Details

2009

  • An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naıve Bayes

    Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, ChunSung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin, and Shou-de Lin, in KDD Cup, 2009.
    Full Text Abstract BibTeX Details
    This paper describes our ensemble of three classifiers for the KDD Cup 2009 challenge. First, we transform the three binary classification tasks into a joint multi-class classification problem, and solve an l1-regularized maximum entropy model under the LIBLINEAR framework. Second, we propose a heterogeneous base learner, which is capable of handling different types of features and missing values, and use AdaBoost to improve the base learner. Finally, we adopt a selective na¨ıve Bayes classifier that automatically groups categorical features and discretizes numerical ones. The parameters are tuned using crossvalidation results rather than the 10% test results on the competition website. Based on the observation that the three positive labels are exclusive, we conduct a post-processing step using the linear SVM to jointly adjust the prediction scores of each classifier on the three tasks. Then, we average these prediction scores with careful validation to get the final outputs. Our final average AUC on the whole test set is 0.8461, which ranks third place in the slow track of KDD Cup 2009.
    @inproceedings{LCCCFHKKLLWYLLL09,
      author = {Lo, Hung-Yi and Chang, Kai-Wei and Chen, Shang-Tse and Chiang, Tsung-Hsien and Ferng, ChunSung and Hsieh, Cho-Jui and Ko, Yi-Kuang and Kuo, Tsung-Ting and Lai, Hung-Che and Lin, Ken-Yi and Wang, Chia-Hsuan and Yu, Hsiang-Fu and Lin, Chih-Jen and Lin, Hsuan-Tien and Lin, Shou-de},
      title = {An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naıve Bayes},
      booktitle = {KDD Cup},
      year = {2009}
    }
    
    Details

2008

  • A Sequential Dual Method for Large Scale Multi-Class Linear SVMs

    S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin, in KDD, 2008.
    Full Text Code Abstract BibTeX Details
    Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features. This paper presents a fast dual method for this training. The main idea is to sequentially traverse through the training set and optimize the dual variables associated with one example at a time. The speed of training is enhanced further by shrinking and cooling heuristics. Experiments indicate that our method is much faster than state of the art solvers such as bundle, cutting plane and exponentiated gradient methods
    @inproceedings{KSCHL08,
      author = {Keerthi, S. Sathiya and Sundararajan, S. and Chang, Kai-Wei and Hsieh, Cho-Jui and Lin, Chih-Jen},
      title = {A Sequential Dual Method for Large Scale Multi-Class Linear SVMs},
      booktitle = {KDD},
      year = {2008}
    }
    
    Details
  • A Dual Coordinate Descent Method for Large-Scale Linear SVM

    Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, Sathia S. Keerthi, and S. Sundararajan, in ICML, 2008.
    Full Text Slides Code Abstract BibTeX Details
    In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1- and L2- loss functions. The proposed method is simple and reaches an ϵ-accurate solution in O(log(1/ϵ)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVMperf , and a recent primal coordinate descent implementation.
    @inproceedings{HCLKS08,
      author = {Hsieh, Cho-Jui and Chang, Kai-Wei and Lin, Chih-Jen and Keerthi, Sathia S. and Sundararajan, S.},
      title = {A Dual Coordinate Descent Method for Large-Scale Linear SVM},
      booktitle = {ICML},
      slides_url = {http://bilbo.cs.illinois.edu/~kchang10/icml_talk.pdf},
      year = {2008}
    }
    
    Details
  • Coordinate Descent Method for Large-scale L2-loss Linear SVM

    Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin, in JMLR, 2008.
    Full Text Code Abstract BibTeX Details
    Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification and natural language processing. In this paper, we propose a novel coordinate descent algorithm for training linear SVM with the L2-loss function. At each step, the proposed method minimizes a one-variable sub-problem while fixing other variables. The sub-problem is solved by Newton steps with the line search technique. The procedure globally converges at the linear rate. As each sub-problem involves only values of a corresponding feature, the proposed approach is suitable when accessing a feature is more convenient than accessing an instance. Experiments show that our method is more efficient and stable than state of the art methods such as Pegasos and TRON.
    @inproceedings{ChangHsLi08,
      author = {Chang, Kai-Wei and Hsieh, Cho-Jui and Lin, Chih-Jen},
      title = {Coordinate Descent Method for Large-scale L2-loss Linear SVM},
      booktitle = {JMLR},
      year = {2008}
    }
    
    Details
  • LIBLINEAR: A Library for Large Linear Classification

    Rong En Fan, Kai-Wei Chang, Cho-Jui Hsieh, X.-R. Wang, and Chih-Jen Lin, in JMLR, 2008.
    Full Text Code Abstract BibTeX Details
    LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.
    @inproceedings{FCHWL08,
      author = {Fan, Rong En and Chang, Kai-Wei and Hsieh, Cho-Jui and Wang, X.-R. and Lin, Chih-Jen},
      title = {LIBLINEAR: A Library for Large Linear Classification},
      booktitle = {JMLR},
      year = {2008}
    }
    
    Details