Classifying Syntactic Errors in Learner Language
Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, Omri Abend · 2020
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.* First two authors contributed equally. 1 Code can be found in github repo GEC UD divergences.Matrices directly mentioned are included in the appendix.