@inproceedings{yang-etal-2025-using,
    title = "Using {NLI} to Identify Potential Collocation Transfer in {L}2 {E}nglish",
    author = "Yang, Haiyin  and
      Liu, Zoey  and
      Wulff, Stefanie",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.bea-1.49/",
    doi = "10.18653/v1/2025.bea-1.49",
    pages = "687--696",
    ISBN = "979-8-89176-270-1",
    abstract = "Identifying instances of first language (L1) transfer {--} the application of the linguistics structures of a speaker{'}s first language to their second language(s) {--} can facilitate second language (L2) learning as it can inform learning and teaching resources, especially when instances of negative transfer (that is, interference) can be identified. While studies of transfer between two languages A and B require a priori linguistic structures to be analyzed with three datasets (data from L1 speakers of language A, L1 speakers of language B, and L2 speakers of A or B), native language identification (NLI) {--} a machine learning task to predict one{'}s L1 based on one{'}s L2 production {--} has the advantage to detect instances of subtle and unpredicted transfer, casting a ``wide net'' to capture patterns of transfer that were missed before (Jarvis and Crossley, 2018). This study aims to apply NLI tasks to find potential instances of transfer of collocations. Our results, compared to previous transfer studies, indicate that NLI can be used to reveal collocation transfer, also in understudied L2 languages."
}