Abstract
The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May. 20, 2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively.- Anthology ID:
- 2020.findings-emnlp.370
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4141–4150
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.370
- DOI:
- 10.18653/v1/2020.findings-emnlp.370
- Cite (ACL):
- Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, and Daxin Jiang. 2020. No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4141–4150, Online. Association for Computational Linguistics.
- Cite (Informal):
- No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension (Wang et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.findings-emnlp.370.pdf
- Data
- Natural Questions