| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
| Outcome: | The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets. |
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End-to-End Self-Debiasing Framework for Robust NLU Training (2021.findings-acl)
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| Challenge: | Existing models incorporate dataset biases leading to strong performance on in-distribution test sets but poor performance on out-of-distortion (OOD) tests. |
| Approach: | They propose a debiasing framework where the shallow representations of the main model are used to derive a bias model and both models are trained simultaneously. |
| Outcome: | The proposed framework outperforms existing approaches on three well-studied NLU tasks while still delivering high in-distribution performance. |
Towards Robustifying NLI Models Against Lexical Dataset Biases (2020.acl-main)
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| Challenge: | Recent studies show that deep learning models exploit dataset biases without deep understanding of the language semantics. |
| Approach: | They propose two methods to debiase models against lexical dataset biases . they use contradiction-word bias and word-overlapping bias as examples . |
| Outcome: | The proposed method removes label bias at embedding level, while the other uses a bag-of-words sub-model to capture features likely to exploit the bias. |
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. |
| Approach: | They propose to tackle this problem by using adversarial training to reduce the bias in sentence representations by using an ensemble of adversaries. |
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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. |
| Approach: | They propose a general probing-based framework that allows for post-hoc interpretation of biases in language models and use an information-theoretic approach to measure the extractability of certain biase . |
| Outcome: | The proposed framework allows for post-hoc interpretation of biases in language models and measures the extractability of certain biase . |
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. |
| Approach: | They propose a method which discourages models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. |
| Outcome: | The proposed method improves on out-of-distribution datasets while maintaining original in-district accuracy. |
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
| Outcome: | The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias. |
When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
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| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
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Towards Debiasing NLU Models from Unknown Biases (2020.emnlp-main)
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| Challenge: | Recent proposed debiasing methods rely on the assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. |
| Approach: | They propose a framework that prevents models from mainly utilizing biases without knowing them in advance. |
| Outcome: | The proposed framework allows existing methods to retain performance improvement on challenge datasets without specifically targeting biases. |
A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
| Outcome: | The proposed method improves learning efficiency and improves overall performance. |
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020.lrec-1)
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| Challenge: | Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning . |
| Approach: | They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities. |
| Outcome: | The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings. |