Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2019

Abstract

Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 2019 January 27 - February 1

Volume

33

First Page

6318

Last Page

6325

Identifier

10.1609/AAAI.V33I01.33016318

Publisher

AAAI Press

City or Country

USA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1609/AAAI.V33I01.33016318

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