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Top cited Papers at Top Conferneces

According to Paper Digest
[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]

  1. On the Paradox of Learning to Reason from Data

    Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, and Guy Van den Broeck., in IJCAI, 2023.
    Full Text Code Abstract BibTeX Details Top-10 cited paper at IJCAI 23 
    Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general. Our result naturally extends to other neural models and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.
    @inproceedings{zhang2023on,
  title = {On the Paradox of Learning to Reason from Data},
  author = {Zhang, Honghua and Li, Liunian Harold and Meng, Tao and Chang, Kai-Wei and den Broeck., Guy Van},
  booktitle = {IJCAI},
  year = {2023}
}
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  2. Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

    Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan, in NeurIPS, 2022.
    Full Text BibTeX Details Top-15 cited paper at NeurIPS 22 
    @inproceedings{lu2022learn,
  title = {Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
  author = {Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Kalyan, Ashwin},
  booktitle = {NeurIPS},
  github_url = {https://github.com/lupantech/ScienceQA},
  year = {2022}
}
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  3. How Much Can CLIP Benefit Vision-and-Language Tasks?

    Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutz, in ICLR, 2022.
    Full Text Code Abstract BibTeX Details Top-10 cited paper at ICLR 22 
    Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks.
    @inproceedings{shen2022how,
  title = { How Much Can CLIP Benefit Vision-and-Language Tasks? },
  author = {Shen, Sheng and Li, Liunian Harold and Tan, Hao and Bansal, Mohit and Rohrbach, Anna and Chang, Kai-Wei and Yao, Zhewei and Keutz, Kurt},
  booktitle = {ICLR},
  year = {2022}
}
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  4. Unified Pre-training for Program Understanding and Generation

    Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in NAACL, 2021.
    Full Text Video Code Abstract BibTeX Details Top-10 cited paper at NAACL 21 
    Code summarization nd generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART’s effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., if block inside an else block is equivalent to else if block) that are crucial to program semantics and thus excels even with limited annotations.
    @inproceedings{ahmad2021unified,
  title = {Unified Pre-training for Program Understanding and Generation},
  author = {Ahmad, Wasi and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei},
  booktitle = {NAACL},
  presentation_id = {https://underline.io/events/122/sessions/4197/lecture/20024-unified-pre-training-for-program-understanding-and-generation},
  year = {2021}
}
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  5. GPT-GNN: Generative Pre-Training of Graph Neural Networks

    Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun, in KDD, 2020.
    Full Text Video Code Abstract BibTeX Details Top-10 cited paper at KDD 20 
    Graph neural networks (GNNs) have been demonstrated to besuccessful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned knowledge to downstream models. In this paper, we present the GPT-GNN’s framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN,which allows the GNN to capture the intrinsic structural and semantic properties of the graph. We factorize the likelihood of graph generation into two components: 1) attribute generation, and 2) edgegeneration. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on thebillion-scale academic graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art base GNN models without pre-training by up to 9.1% across different downstream tasks.
    @inproceedings{hu2020gptgnn,
  author = {Hu, Ziniu and Dong, Yuxiao and Wang, Kuansan and Chang, Kai-Wei and Sun, Yizhou},
  title = {GPT-GNN: Generative Pre-Training of Graph Neural Networks},
  booktitle = {KDD},
  slide_url = {https://acbull.github.io/pdf/gpt.pptx},
  year = {2020}
}
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  6. 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 Code Abstract BibTeX Details Top-10 cited paper at EMNLP 18 
    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}
}
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  7. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2018.
    Full Text Poster Code Abstract BibTeX Details Top-10 cited paper at NAACL 18 
    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 = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
  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}
}
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  8. 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; Top-10 cited paper at EMNLP 17 
    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},
  year = {2017}
}
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  9. 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 Top-10 cited paper at NeurIPS 16 
    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}
}
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  10. 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 Top-10 cited paper at ICML 08 
    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 e-accurate solution in O(log(1/e)) 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},
  year = {2008}
}
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