Debiasing Methods in Natural Language Understanding Make Bias More Accessible

Michael Mendelson, Yonatan Belinkov


Abstract
Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions. An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model’s inner representations. We 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 biases from the model’s representations. We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations.
Anthology ID:
2021.emnlp-main.116
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1545–1557
Language:
URL:
https://aclanthology.org/2021.emnlp-main.116
DOI:
10.18653/v1/2021.emnlp-main.116
Bibkey:
Cite (ACL):
Michael Mendelson and Yonatan Belinkov. 2021. Debiasing Methods in Natural Language Understanding Make Bias More Accessible. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1545–1557, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Debiasing Methods in Natural Language Understanding Make Bias More Accessible (Mendelson & Belinkov, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.116.pdf
Code
 technion-cs-nlp/bias-probing
Data
FEVERSNLI