Abstract
Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer. In this paper, we introduce the Dynamic Self-attention Network (DynSAN) for multi-passage reading comprehension task, which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. The core module of the dynamic self-attention is a proposed gated token selection mechanism, which dynamically selects important tokens from a sequence. These chosen tokens will attend to each other via a self-attention mechanism to model long-range dependencies. Besides, convolutional layers are combined with the dynamic self-attention to enhance the model’s capacity of extracting local semantic. The experimental results show that the proposed DynSAN achieves new state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets. Further ablation study also validates the effectiveness of our model components.- Anthology ID:
- P19-1218
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2252–2262
- Language:
- URL:
- https://aclanthology.org/P19-1218
- DOI:
- 10.18653/v1/P19-1218
- Cite (ACL):
- Yimeng Zhuang and Huadong Wang. 2019. Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2252–2262, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (Zhuang & Wang, ACL 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/P19-1218.pdf
- Data
- QUASAR, QUASAR-T, SQuAD, SearchQA, WikiHop