Search-based Neural Structured Learning for Sequential Question Answering
Abstract
Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.- Anthology ID:
- P17-1167
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1821–1831
- Language:
- URL:
- https://aclanthology.org/P17-1167
- DOI:
- 10.18653/v1/P17-1167
- Cite (ACL):
- Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017. Search-based Neural Structured Learning for Sequential Question Answering. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1821–1831, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Search-based Neural Structured Learning for Sequential Question Answering (Iyyer et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P17-1167.pdf
- Data
- SQA, WikiTableQuestions