@inproceedings{baranes-etal-2026-beyond,
    title = "Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries",
    author = "Baranes, Marion  and
      Hennequin, Romain  and
      Epure, Elena V.",
    editor = "Epure, Elena V.  and
      Oramas, Sergio  and
      Doh, SeungHeon  and
      Ramoneda, Pedro  and
      Kruspe, Anna  and
      Sordo, Mohamed",
    booktitle = "Proceedings of the 4th Workshop on {NLP} for Music and Audio ({NLP}4{M}us{A} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.nlp4musa-1.4/",
    pages = "20--26",
    ISBN = "979-8-89176-369-2",
    abstract = "Although annotated music descriptor datasets for user queries are increasingly common, few consider the user{'}s intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles.We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems."
}