Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

Yu Chen, Lingfei Wu, Mohammed J. Zaki


Abstract
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, relation paths and context). In this work, we propose to directly model the two-way flow of interactions between the questions and the KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring no external resources and only very few hand-crafted features, on the WebQuestions benchmark, our method significantly outperforms existing information-retrieval based methods, and remains competitive with (hand-crafted) semantic parsing based methods. Also, since we use attention mechanisms, our method offers better interpretability compared to other baselines.
Anthology ID:
N19-1299
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2913–2923
Language:
URL:
https://www.aclweb.org/anthology/N19-1299
DOI:
10.18653/v1/N19-1299
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1299.pdf
Video:
 https://vimeo.com/356071812