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.
@inproceedings{DBLP:conf/conll/ChoshenNBA20,
author = {Leshem Choshen and
Dmitry Nikolaev and
Yevgeni Berzak and
Omri Abend},
editor = {Raquel Fern{\'{a}}ndez and
Tal Linzen},
title = {Classifying Syntactic Errors in Learner Language},
booktitle = {Proceedings of the 24th Conference on Computational Natural Language
Learning, CoNLL 2020, Online, November 19-20, 2020},
pages = {97--107},
publisher = {Association for Computational Linguistics},
year = {2020},
url = {https://doi.org/10.18653/v1/2020.conll-1.7},
doi = {10.18653/V1/2020.CONLL-1.7},
timestamp = {Sat, 30 Sep 2023 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/conll/ChoshenNBA20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.