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 |
|---|---|
| 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 |
|---|
Conference
| Conference | 24th Conference on Computational Natural Language Learning, CoNLL 2020 |
|---|---|
| City | Virtual, Online |
| Period | 19/11/20 → 20/11/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics.
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