Universal Morpho-syntactic Parsing and the Contribution of Lexica: Analyzing the OnLP lab submission to the conll 2018 shared task

Amit Seker, Amir More, Reut Tsarfaty

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

4 Scopus citations

Abstract

We present the contribution of the ONLP lab at the Open University of Israel to the CONLL 2018 UD SHARED TASK on MULTILINGUAL PARSING FROM RAW TEXT TO UNIVERSAL DEPENDENCIES. Our contribution is based on a transition-based parser called yap: yet another parser which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used yap's standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In a follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a morphologically-rich and low-resource language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica.

Original languageEnglish
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)
Pages208-215
Number of pages8
ISBN (Electronic)9781948087827
DOIs
StatePublished - 2018
Externally publishedYes
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

Funding

We thank the CoNLL Shared Task Organizing Committee for their hard work and their timely support. We also thank the TIRA platform team (Potthast et al., 2014) for providing a system that facilitates competition and reproducible research. The research towards this shared task submission has been supported by the European Research Council, ERC-StG-2015 Grant 677352, and by an Israel Science Foundation (ISF) Grant 1739/26, for which we are grateful.

FundersFunder number
European Research CouncilERC-StG-2015, 677352
Israel Science Foundation1739/26

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