@inproceedings{shukla-etal-2025-comumdr,
    title = "{C}o{M}u{MDR}: Code-mixed Multi-modal Multi-domain corpus for Discourse pa{R}sing in conversations",
    author = "Shukla, Divyaksh  and
      Baviskar, Ritesh  and
      Gohil, Dwijesh  and
      Tiwari, Aniket  and
      Shree, Atul  and
      Modi, Ashutosh",
    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/landing_page/2025.findings-acl.565/",
    pages = "10834--10849",
    ISBN = "979-8-89176-256-5",
    abstract = "Discourse parsing is an important task useful for NLU applications such as summarization, machine comprehension, and emotion recognition. The current discourse parsing datasets based on conversations consists of written English dialogues restricted to a single domain. In this resource paper, we introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations. The corpus (code-mixed in Hindi and English) has both audio and transcribed text and is annotated with nine discourse relations. We experiment with various SoTA baseline models; the poor performance of SoTA models highlights the challenges of multi-domain code-mixed corpus, pointing towards the need for developing better models for such realistic settings."
}