Universal dependency parsing with a general transition-based DAG parser

Daniel Hershcovich, Omri Abend, Ari Rappoport

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

6 Scopus citations

Abstract

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning. Our code is available at https://github.com/CoNLL-UD-2018/HUJI.

Original languageAmerican English
Title of host publicationCoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task
Subtitle of host publicationMultilingual Parsing from Raw Text to Universal Dependencies
PublisherAssociation for Computational Linguistics (ACL)
Pages103-112
Number of pages10
ISBN (Electronic)9781948087827
DOIs
StatePublished - 2018
Event2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018 - Brussels, Belgium
Duration: 31 Oct 20181 Nov 2018

Publication series

NameCoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Conference

Conference2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018
Country/TerritoryBelgium
CityBrussels
Period31/10/181/11/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computational Linguistics

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