Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2019

Abstract

Making use of knowledge bases to answer questions (KBQA) is a key direction in question answering systems. Researchers have developed a diverse range of methods to address this problem, but there are still some limitations with the existing methods. Specifically, the existing neural network-based methods for KBQA have not taken advantage of the recent “matching-aggregation” framework for the sequence matching, and when representing a candidate answer entity, they may not choose the most useful context of the candidate for matching. In this paper, we explore the use of a “matching-aggregation” framework to match candidate answers with questions. We further make use of question-specific contextual relations to enhance the representations of candidate answer entities. Our complete method is able to achieve state-of-the-art performance on two benchmark datasets: WebQuestions and SimpleQuestions.

Keywords

Artificial intelligence, natural language processing, knowledge base question answering

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE/ACM Transactions on Audio, Speech and Language Processing

Volume

27

Issue

10

First Page

1629

Last Page

1638

ISSN

2329-9290

Identifier

10.1109/TASLP.2019.2926125

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1109/TASLP.2019.2926125

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