@inproceedings{tang-etal-2021-berts,
    title = "Are {BERT}s Sensitive to Native Interference in {L}2 Production?",
    author = "Tang, Zixin  and
      Mitra, Prasenjit  and
      Reitter, David",
    editor = "Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Rumshisky, Anna  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the Second Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.insights-1.6/",
    doi = "10.18653/v1/2021.insights-1.6",
    pages = "36--41",
    abstract = "With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners' native languages. Results showed neural models can learn to represent and detect such native language impacts, but multilingually trained models have no advantage in doing so."
}