Classifying Syntactic Errors in Learner Language

Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, Omri Abend

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

12 Scopus citations

Abstract

We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.

Original languageAmerican English
Title of host publicationCoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference
EditorsRaquel Fernandez, Tal Linzen
PublisherAssociation for Computational Linguistics (ACL)
Pages97-107
Number of pages11
ISBN (Electronic)9781952148637
StatePublished - 2020
Event24th Conference on Computational Natural Language Learning, CoNLL 2020 - Virtual, Online
Duration: 19 Nov 202020 Nov 2020

Publication series

NameCoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference24th Conference on Computational Natural Language Learning, CoNLL 2020
CityVirtual, Online
Period19/11/2020/11/20

Bibliographical note

Publisher Copyright:
© 2020 Association for Computational Linguistics.

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