Abstract
Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion anSwering (MCROSS) method for extractive QA. Specifically, MCROSS introduces a momentum contrastive learning framework to align the answer probability between cloze-like and natural query-passage sample pairs. Hence, the pre-trained models can better transfer the knowledge learned in cloze-like samples to answering natural questions. Experimental results on three benchmarking QA datasets show that our method achieves noticeable improvement compared with all baselines in both supervised and zero-shot scenarios.- Anthology ID:
- 2022.emnlp-main.291
- Original:
- 2022.emnlp-main.291v1
- Version 2:
- 2022.emnlp-main.291v2
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4324–4330
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.291
- DOI:
- 10.18653/v1/2022.emnlp-main.291
- Cite (ACL):
- Minda Hu, Muzhi Li, Yasheng Wang, and Irwin King. 2022. Momentum Contrastive Pre-training for Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4324–4330, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Momentum Contrastive Pre-training for Question Answering (Hu et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.emnlp-main.291.pdf