Improved Neural Relation Detection for Knowledge Base Question Answering

Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cicero dos Santos, Bing Xiang, Bowen Zhou


Abstract
Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different levels of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to make the two components enhance each other. Our experimental results show that our approach not only achieves outstanding relation detection performance, but more importantly, it helps our KBQA system achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.
Anthology ID:
P17-1053
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:
571–581
Language:
URL:
https://www.aclweb.org/anthology/P17-1053
DOI:
10.18653/v1/P17-1053
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1053.pdf
Note:
 P17-1053.Notes.pdf
Dataset:
 P17-1053.Datasets.zip
Video:
 https://vimeo.com/234956455