Negative language transfer in learner English: A new dataset
Leticia Farias Wanderley, Nicole Zhao, Carrie Demmans Epp
Abstract
Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge. This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners’ first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners’ first language and in providing feedback that is informed by the cause of an error.
BibTeX
@inproceedings{farias-wanderley-etal-2021-negative,
title = "Negative language transfer in learner {E}nglish: A new dataset",
author = "Farias Wanderley, Leticia and
Zhao, Nicole and
Demmans Epp, Carrie",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.251/",
doi = "10.18653/v1/2021.naacl-main.251",
pages = "3129--3142"
}