An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge

Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, Jun Zhao

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Abstract
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the cross-attention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.
Anthology ID:
P17-1021
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:
221–231
Language:
URL:
https://aclanthology.org/P17-1021
DOI:
10.18653/v1/P17-1021
Bibkey:
Cite (ACL):
Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, and Jun Zhao. 2017. An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 221–231, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge (Hao et al., ACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P17-1021.pdf
Data
WebQuestions