@inproceedings{chun-xue-2025-modal,
    title = "Modal Dependency Parsing via Biaffine Attention with Self-Loop",
    author = "Chun, Jayeol  and
      Xue, Nianwen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.findings-acl.1093/",
    doi = "10.18653/v1/2025.findings-acl.1093",
    pages = "21226--21238",
    ISBN = "979-8-89176-256-5",
    abstract = "A modal dependency structure represents a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty, thereby establishing factuality in an event-centric approach. Obtaining such graphs defines the task of modal dependency parsing, which involves event and source identification along with the modal relations between them. In this paper, we propose a simple yet effective solution based on biaffine attention that specifically optimizes against the domain-specific challenges of modal dependency parsing by integrating self-loop. We show that our approach, when coupled with data augmentation by leveraging the Large Language Models to translate annotations from one language to another, outperforms the previous state-of-the-art on English and Chinese datasets by 2{\%} and 4{\%} respectively."
}