Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, Gong Cheng
Abstract
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.- Anthology ID:
- 2022.naacl-main.239
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3272–3287
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.239
- DOI:
- 10.18653/v1/2022.naacl-main.239
- Cite (ACL):
- Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, and Gong Cheng. 2022. Clues Before Answers: Generation-Enhanced Multiple-Choice QA. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3272–3287, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Clues Before Answers: Generation-Enhanced Multiple-Choice QA (Huang et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.naacl-main.239.pdf
- Code
- nju-websoft/genmc
- Data
- CommonsenseQA, QASC