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 language | English |
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Title of host publication | CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference |
Editors | Raquel Fernandez, Tal Linzen |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 97-107 |
Number of pages | 11 |
ISBN (Electronic) | 9781952148637 |
DOIs | |
State | Published - 2020 |
Event | 24th Conference on Computational Natural Language Learning, CoNLL 2020 - Virtual, Online Duration: 19 Nov 2020 → 20 Nov 2020 |
Publication series
Name | CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference |
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Conference
Conference | 24th Conference on Computational Natural Language Learning, CoNLL 2020 |
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City | Virtual, Online |
Period | 19/11/20 → 20/11/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics.