Native Language Identification in Texts: A Survey
Dhiman Goswami, Sharanya Thilagan, Kai North, Shervin Malmasi, Marcos Zampieri
Abstract
We present the first comprehensive survey of Native Language Identification (NLI) applied to texts. NLI is the task of automatically identifying an author’s native language (L1) based on their second language (L2) production. NLI is an important task with practical applications in second language teaching and NLP. The task has been widely studied for both text and speech, particularly for L2 English due to the availability of suitable corpora. Speech-based NLI relies heavily on accent modeled by pronunciation patterns and prosodic cues while text-based NLI relies primarily on modeling spelling errors and grammatical patterns that reveal properties of an individuals’ L1 influencing L2 production. We survey over one hundred papers on the topic including the papers associated with the NLI and INLI shared tasks. We describe several text representations and computational techniques used in text-based NLI. Finally, we present a comprehensive account of publicly available datasets used for the task thus far.
BibTeX
@inproceedings{goswami-etal-2024-native,
title = "Native Language Identification in Texts: A Survey",
author = "Goswami, Dhiman and
Thilagan, Sharanya and
North, Kai and
Malmasi, Shervin and
Zampieri, Marcos",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.173/",
doi = "10.18653/v1/2024.naacl-long.173",
pages = "3149--3160"
}