Towards Debiasing NLU Models from Unknown Biases

Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych


Abstract
NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However, these methods rely on a major assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance. The proposed framework is general and complementary to the existing debiasing methods. We show that it allows these existing methods to retain the improvement on the challenge datasets (i.e., sets of examples designed to expose models’ reliance on biases) without specifically targeting certain biases. Furthermore, the evaluation suggests that applying the framework results in improved overall robustness.
Anthology ID:
2020.emnlp-main.613
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7597–7610
Language:
URL:
https://aclanthology.org/2020.emnlp-main.613
DOI:
10.18653/v1/2020.emnlp-main.613
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.613.pdf
Video:
 https://slideslive.com/38938901