Native-language identification: Difference between revisions

Revision as of 05:33, 11 November 2022

Latest revision as of 19:09, 11 September 2023

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NLI works under the assumption that an author's L1 will dispose them towards particular language production patterns in their L2, as influenced by their native language. This relates to cross-linguistic influence (CLI), a key topic in the field of second-language acquisition (SLA) that analyzes transfer effects from the L1 on later learned languages.

Using large-scale English data, NLI methods achieve over 80% accuracy in predicting the native language of texts written by authors from 11 different L1 backgrounds.<ref>Shervin Malmasi, Keelan Evanini, Aoife Cahill, Joel Tetreault, Robert Pugh, Christopher Hamill, Diane Napolitano, and Yao Qian. 2017. [https://aclanthology.org/W17-5007/ "A Report on the 2017 Native Language Identification Shared Task"].pdf {{BareIn URLProceedings PDF|date=Marchof 2022}}the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 62–75, Copenhagen, Denmark. Association for Computational Linguistics.</ref> This can be compared to a baseline of 9% for choosing randomly.

==Applications==