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Title
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A Deep Generative Approach to Native Language Identification
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Author
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Abstract
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| 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. |
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Language
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English
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Source (book)
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Proceedings of the 28th International Conference on Computational Linguistics
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Publication
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International Committee on Computational Linguistics
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2020
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ISBN
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978-1-952148-28-6
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DOI
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10.18653/V1/2020.COLING-MAIN.159
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Volume/pages
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p. 1778-1783
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Full text (Publisher's DOI)
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