Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries

Marion Baranes; Romain Hennequin; Elena V. Epure

Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries

Marion Baranes, Romain Hennequin, Elena V. Epure

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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.

Anthology ID:: 2026.nlp4musa-1.4
Volume:: Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026)
Month:: March
Year:: 2026
Address:: Rabat, Morocco
Editors:: Elena V. Epure, Sergio Oramas, SeungHeon Doh, Pedro Ramoneda, Anna Kruspe, Mohamed Sordo
Venues:: NLP4MusA | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 20–26
Language:
URL:: https://aclanthology.org/2026.nlp4musa-1.4/
DOI:
Bibkey:
Cite (ACL):: Marion Baranes, Romain Hennequin, and Elena V. Epure. 2026. Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries. In Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026), pages 20–26, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):: Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries (Baranes et al., NLP4MusA 2026)
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PDF:: https://aclanthology.org/2026.nlp4musa-1.4.pdf

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@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://aclanthology.org/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."
}

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%0 Conference Proceedings
%T Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries
%A Baranes, Marion
%A Hennequin, Romain
%A Epure, Elena V.
%Y Epure, Elena V.
%Y Oramas, Sergio
%Y Doh, SeungHeon
%Y Ramoneda, Pedro
%Y Kruspe, Anna
%Y Sordo, Mohamed
%S Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-369-2
%F baranes-etal-2026-beyond
%X 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.
%U https://aclanthology.org/2026.nlp4musa-1.4/
%P 20-26

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Markdown (Informal)

[Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries](https://aclanthology.org/2026.nlp4musa-1.4/) (Baranes et al., NLP4MusA 2026)

Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries (Baranes et al., NLP4MusA 2026)

ACL

Marion Baranes, Romain Hennequin, and Elena V. Epure. 2026. Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries. In Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026), pages 20–26, Rabat, Morocco. Association for Computational Linguistics.