Share this page:

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers

Jieyu Zhao, Xuezhi Wang, Yao Qin, Jilin Chen, and Kai-Wei Chang, in EMNLP-Finding (short), 2022.

Download the full text

Abstract

Large pre-trained language models have shown remarkable performance over the past few years. These models, however, sometimes learn superficial features from the dataset and cannot generalize to the distributions that are dissimilar to the training scenario. There have been several approaches proposed to reduce model’s reliance on these bias features which can improve model robustness in the out-of-distribution setting. However, existing methods usually use a fixed low-capacity model to deal with various bias features, which ignore the learnability of those features. In this paper, we analyze a set of existing bias features and demonstrate there is no single model that works best for all the cases. We further show that by choosing an appropriate bias model, we can obtain a better robustness result than baselines with a more sophisticated model design.

Bib Entry

@inproceedings{zhao2022investigating,
  title = {	Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers},
  author = {Zhao, Jieyu and Wang, Xuezhi and Qin, Yao and Chen, Jilin and Chang, Kai-Wei},
  booktitle = {EMNLP-Finding (short)},
  year = {2022}
}

Related Publications

  1. VideoCon: Robust video-language alignment via contrast captions, CVPR, 2024
  2. CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning, ICCV, 2023
  3. Red Teaming Language Model Detectors with Language Models, TACL, 2023
  4. ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation, EMNLP, 2022
  5. Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations, EMNLP-Finding (short), 2022
  6. Improving the Adversarial Robustness of NLP Models by Information Bottleneck, ACL-Finding, 2022
  7. Searching for an Effiective Defender: Benchmarking Defense against Adversarial Word Substitution, EMNLP, 2021
  8. On the Transferability of Adversarial Attacks against Neural Text Classifier, EMNLP, 2021
  9. Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble, ACL, 2021
  10. Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation, NAACL, 2021
  11. Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs, NeurIPS, 2020
  12. On the Robustness of Language Encoders against Grammatical Errors, ACL, 2020
  13. Robustness Verification for Transformers, ICLR, 2020
  14. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification, EMNLP, 2019
  15. Retrofitting Contextualized Word Embeddings with Paraphrases, EMNLP (short), 2019
  16. Generating Natural Language Adversarial Examples, EMNLP (short), 2018