Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers

Oleksandr Harust, Yugo Murawaki, Sadao Kurohashi


Abstract
We propose a novel task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers. This task is highly challenging mainly because 1) the combinatorial nature of expressions prevents us from choosing candidate expressions a priori and 2) the distributions of the two types of texts overlap considerably. Our solution to the first problem is to combine a powerful neural network-based classifier of sentence-level nativeness with an explainability method that measures an approximate contribution of a given expression to the classifier’s prediction. To address the second problem, we introduce a special label neutral and reformulate the classification task as complementary-label learning. Our crowdsourcing-based evaluation and in-depth analysis suggest that our method successfully uncovers linguistically interesting usages distinctive of native speech.
Anthology ID:
2020.coling-main.514
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5843–5854
Language:
URL:
https://aclanthology.org/2020.coling-main.514
DOI:
10.18653/v1/2020.coling-main.514
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.514.pdf