Abstract
Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. %across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.- Anthology ID:
- I17-1096
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 957–966
- Language:
- URL:
- https://aclanthology.org/I17-1096
- DOI:
- Cite (ACL):
- Yelong Shen, Xiaodong Liu, Kevin Duh, and Jianfeng Gao. 2017. An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 957–966, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks (Shen et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/I17-1096.pdf
- Data
- MS MARCO, SQuAD