@inproceedings{farias-wanderley-demmans-epp-2021-identifying,
title = "Identifying negative language transfer in learner errors using {POS} information",
author = "Farias Wanderley, Leticia and
Demmans Epp, Carrie",
editor = "Burstein, Jill and
Horbach, Andrea and
Kochmar, Ekaterina and
Laarmann-Quante, Ronja and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Yannakoudakis, Helen and
Zesch, Torsten",
booktitle = "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bea-1.7",
pages = "64--74",
abstract = "A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.",
}
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<abstract>A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.</abstract>
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%0 Conference Proceedings
%T Identifying negative language transfer in learner errors using POS information
%A Farias Wanderley, Leticia
%A Demmans Epp, Carrie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Kochmar, Ekaterina
%Y Laarmann-Quante, Ronja
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Yannakoudakis, Helen
%Y Zesch, Torsten
%S Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F farias-wanderley-demmans-epp-2021-identifying
%X A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.
%U https://aclanthology.org/2021.bea-1.7
%P 64-74
Markdown (Informal)
[Identifying negative language transfer in learner errors using POS information](https://aclanthology.org/2021.bea-1.7) (Farias Wanderley & Demmans Epp, BEA 2021)
ACL