Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
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| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |
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| Challenge: | Existing methods to evaluate NLP models' weaknesses are limited by “hypothesis-only” tests and CheckLists. |
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Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020.acl-main)
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| Challenge: | a growing number of studies address the effect of bias on predictions, but no unifying framework exists . a general phenomenon of biased predictive models in NLP is not recent, authors say . |
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End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
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Debiasing Methods in Natural Language Understanding Make Bias More Accessible (2021.emnlp-main)
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| Challenge: | Recent debiasing methods in natural language understanding improve performance on out-of-distribution datasets by pressuring models into making unbiased predictions. |
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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (D18-1)
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Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020.emnlp-main)
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| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
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A Closer Look at Data Bias in Neural Extractive Summarization Models (D19-54)
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| Challenge: | In this paper, we examine the generalization behaviour of summarization models . we propose several properties of datasets that matter for generalization . |
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Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance (2020.acl-main)
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| Challenge: | Recent studies show that pre-trained language models rely heavily on idiosyncratic biases of datasets. |
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Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
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Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
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