Classifying Syntactic Errors in Learner Language

Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, Omri Abend

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

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 languageEnglish
Title of host publicationProceedings of the 24th Conference on Computational Natural Language Learning
EditorsRaquel Fernández, Tal Linzen
Place of PublicationOnline
PublisherAssociation for Computational Linguistics (ACL)
Pages97-107
Number of pages11
ISBN (Electronic)978-1-952148-63-7
DOIs
StatePublished - Nov 2020
Event24th Conference on Computational Natural Language Learning - Online
Duration: 19 Nov 202020 Nov 2020
Conference number: 24
https://aclanthology.org/volumes/2020.conll-1/

Conference

Conference24th Conference on Computational Natural Language Learning
Period19/11/2020/11/20
Internet address

Keywords

  • learner language
  • error-classification systems
  • Grammatical Error Correction
  • GEC

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