From SPMRL to NMRL: What did we learn (and Unlearn) in a decade of parsing Morphologically-Rich Languages (MRLs)?

Reut Tsarfaty, Dan Bareket, Stav Klein, Amit Seker

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

22 Scopus citations

Abstract

It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs). Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch a strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language.

Original languageEnglish
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages7396-7408
Number of pages13
ISBN (Electronic)9781952148255
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period5/07/2010/07/20

Bibliographical note

Publisher Copyright:
© 2020 Association for Computational Linguistics

Funding

We thank Clara Vania, Adam Lopez, and members of the Edinburgh-NLP seminar, Yoav Goldberg, Ido Dagan, and members of the BIU-NLP seminar, for intriguing discussions on earlier presentations of this work. This research is kindly supported by the Israel Science Foundation (ISF), grant No. 1739/16, and by the European Research Council (ERC), under the Europoean Union Horizon 2020 research and innovation programme, grant No. 677352.

FundersFunder number
Clara Vania
Europoean Union Horizon 2020 research and innovation programme
Horizon 2020 Framework Programme677352
European Commission
Israel Science Foundation1739/16

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