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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- Cite (ACL):
- Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020. Towards Debiasing NLU Models from Unknown Biases. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7597–7610, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards Debiasing NLU Models from Unknown Biases (Utama et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.emnlp-main.613.pdf
- Code
- UKPLab/emnlp2020-debiasing-unknown
- Data
- GLUE, MultiNLI, PAWS, SICK