Dynamic entity representation with max-pooling improves machine reading

Sosuke Kobayashi, Ran Tian, Naoaki Okazaki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Citations (Scopus)

Abstract

We propose a novel neural network model for machine reading, DER Network, which explicitly implements a reader building dynamic meaning representations for entities by gathering and accumulating information around the entities as it reads a document. Evaluated on a recent large scale dataset (Hermann et al., 2015), our model exhibits better results than previous research, and we find that max-pooling is suited for modeling the accumulation of information on entities. Further analysis suggests that our model can put together multiple pieces of information encoded in different sentences to answer complicated questions. Our code for the model is available at https://github.com/soskek/der-network.

Original languageEnglish
Title of host publication2016 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages850-855
Number of pages6
ISBN (Electronic)9781941643914
DOIs
Publication statusPublished - 2016
Event15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - San Diego, United States
Duration: 2016 Jun 122016 Jun 17

Publication series

Name2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference

Conference

Conference15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Country/TerritoryUnited States
CitySan Diego
Period16/6/1216/6/17

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