Learning to Answer Questions by Understanding Using Entity-Based Memory Network

Xun Wang, Katsuhito Sudoh, Masaaki Nagata, Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces a novel neural network model for question answering, the entity-based memory network. It enhances neural networks’ ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities’ states. These entities’ states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Entities in this model are regard as the basic units that carry information and construct text. Information carried by text are encoded in the states of entities. Hence text can be best understood by analysing its containing entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.

Original languageEnglish
Pages (from-to)799-808
Number of pages10
JournalComputacion y Sistemas
Volume21
Issue number4
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Entity memory network
  • Question answering
  • Text comprehension

ASJC Scopus subject areas

  • Computer Science(all)

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