@inproceedings{acharya-etal-2025-tracing,
    title = "Tracing {L}1 Interference in {E}nglish Learner Writing: A Longitudinal Corpus with Error Annotations",
    author = "Acharya, Poorvi  and
      Liebl, J. Elizabeth  and
      Goswami, Dhiman  and
      North, Kai  and
      Zampieri, Marcos  and
      Anastasopoulos, Antonios",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-diogo-silva-nova/2025.emnlp-main.766/",
    doi = "10.18653/v1/2025.emnlp-main.766",
    pages = "15157--15178",
    ISBN = "979-8-89176-332-6",
    abstract = "Language transfer is an important topic of research in second language acquisition and computational linguistics. The availability of suitable learner corpora is paramount for the study of second language acquisition (SLA) and language transfer. However, curating learner corpora is a challenging endeavor as high quality learner data is rarely publicly available. This results in only a few such corpora available to the community. To address this important gap, in this paper we present LENS, a novel English learner corpus with longitudinal data which enables researchers to investigate language learning over time. LENS contains 687 instances written by speakers of 15 different L1s. We use LENS two perform two important tasks at the intersection of SLA and Computational Linguistics: (1) Native Language Identification (NLI); and (2) an evaluation of large language models as a tool for high-precision, semi-automated annotation of L1 interference features."
}