A Deep Generative Approach to Native Language Identification

Ehsan Lotfi, Ilia Markov, Walter Daelemans


Abstract
Native language identification (NLI) – identifying the native language (L1) of a person based on his/her writing in the second language (L2) – is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.
Anthology ID:
2020.coling-main.159
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1778–1783
Language:
URL:
https://aclanthology.org/2020.coling-main.159
DOI:
10.18653/v1/2020.coling-main.159
Bibkey:
Cite (ACL):
Ehsan Lotfi, Ilia Markov, and Walter Daelemans. 2020. A Deep Generative Approach to Native Language Identification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1778–1783, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Deep Generative Approach to Native Language Identification (Lotfi et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.159.pdf