Work

Classifying Syntactic Errors in Learner Language

Year: 2020

Type: conference-paper

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.* First two authors contributed equally. 1 Code can be found in github repo GEC UD divergences.Matrices directly mentioned are included in the appendix.

Source: Conference on Computational Natural Language Learning

DOI: 10.18653/v1/2020.conll-1.7

Authors: Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, Omri Abend

Institutions: Hebrew University of Jerusalem, Stockholm University, Massachusetts Institute of Technology

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Israel Science Foundation

Award: G5348532491