Distance-free modeling of multi-predicate interactions in end-to-end Japanese predicate-argument structure analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese. In this paper, we propose new Japanese PAS analysis models that integrate the label prediction information of arguments in multiple PASs by extending the input and last layers of a standard deep bidirectional recurrent neural network (bi-RNN) model. In these models, using the mechanisms of pooling and attention, we aim to directly capture the potential interactions among multiple PASs, without being disturbed by the word order and distance. Our experiments show that the proposed models improve the prediction accuracy specifically for cases where the predicate and argument are in an indirect dependency relation and achieve a new state of the art in the overall F1 on a standard benchmark corpus.

Original languageEnglish
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
EditorsEmily M. Bender, Leon Derczynski, Pierre Isabelle
PublisherAssociation for Computational Linguistics (ACL)
Pages94-106
Number of pages13
ISBN (Electronic)9781948087506
Publication statusPublished - 2018
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: 2018 Aug 202018 Aug 26

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period18/8/2018/8/26

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

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