Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering

Kun Xu, Yuxuan Lai, Yansong Feng, Zhiguo Wang

Abstract
Traditional Key-value Memory Neural Networks (KV-MemNNs) are proved to be effective to support shallow reasoning over a collection of documents in domain specific Question Answering or Reading Comprehension tasks. However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory. In this paper, we propose a novel mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. To achieve this, we design a new query updating strategy to mask previously-addressed memory information from the query representations, and introduce a novel STOP strategy to avoid invalid or repeated memory reading without strong annotation signals. This also enables KV-MemNNs to produce structured queries and work in a semantic parsing fashion. Experimental results on benchmark datasets show that our solution, trained with question-answer pairs only, can provide conventional KV-MemNNs models with better reasoning abilities on complex questions, and achieve state-of-art performances.
Anthology ID:
N19-1301
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
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2937–2947
Language:
URL:
https://aclanthology.org/N19-1301
DOI:
10.18653/v1/N19-1301
Bibkey:
Cite (ACL):
Kun Xu, Yuxuan Lai, Yansong Feng, and Zhiguo Wang. 2019. Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering. In 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), pages 2937–2947, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (Xu et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-2024-clasp/N19-1301.pdf
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