Edinburgh Research Explorer

UParse: the Edinburgh system for the CoNLL 2017 UD shared task

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

Original languageEnglish
Title of host publicationProceedings of the Conference on Computational Natural Language Learning Shared Task (CoNLL 17)
PublisherAssociation for Computational Linguistics (ACL)
Pages100-110
Number of pages11
ISBN (Print)978-1-945626-70-8
DOIs
Publication statusPublished - 4 Aug 2017
Event Conference on Computational Natural Language Learning Shared Task 2017 - Vancouver, Canada
Duration: 3 Aug 20174 Aug 2017
http://www.conll.org/2017

Conference

Conference Conference on Computational Natural Language Learning Shared Task 2017
Abbreviated titleCoNLL 2017
CountryCanada
CityVancouver
Period3/08/174/08/17
Internet address

Abstract

This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for lowresource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).

Event

Conference on Computational Natural Language Learning Shared Task 2017

3/08/174/08/17

Vancouver, Canada

Event: Conference

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