Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2019

Abstract

Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three commonly used benchmark datasets show that our method consistently works well and outperforms the previous state of the art on two datasets.

Keywords

Natural Language Processing, Question Answering, Knowledge-based Learning

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

First Page

5046

Last Page

5052

Identifier

10.24963/ijcai.2019/701

City or Country

Macao, China

Additional URL

https://doi.org/10.24963/ijcai.2019/701

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