Search-based Neural Structured Learning for Sequential Question Answering

Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P17-1167.pdf
Presentation:
 P17-1167.Presentation.pptx
Data
SQAWikiTableQuestions