Neural Greedy Constituent Parsing with Dynamic Oracles

Maximin Coavoux, Benoit Crabbe

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

Abstract

Dynamic oracle training has shown substantial improvements for dependency parsing in various settings, but has not been explored for constituent parsing. The present article introduces a dynamic oracle for transition-based constituent parsing. Experiments on the 9 languages of the SPMRL dataset show that a neural greedy parser with morphological features, trained with a dynamic oracle, leads to accuracies comparable with the best non-reranking and non-ensemble parsers.
Original languageEnglish
Title of host publication54th Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics (ACL)
Pages172-182
Number of pages11
DOIs
Publication statusPublished - 1 Aug 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016
https://mirror.aclweb.org/acl2016/

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
CountryGermany
CityBerlin
Period7/08/1612/08/16
Internet address

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