Classifying Syntactic Errors in Learner Language

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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.

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Choshen, L., Nikolaev, D., Berzak, Y., & Abend, O. (2020). Classifying Syntactic Errors in Learner Language. In CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference (pp. 97–107). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.conll-1.7

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