@inproceedings{zang-zhang-2024-interpretation,
    title = "The Interpretation Gap in Text-to-Music Generation Models",
    author = "Zang, Yongyi  and
      Zhang, Yixiao",
    editor = "Kruspe, Anna  and
      Oramas, Sergio  and
      Epure, Elena V.  and
      Sordo, Mohamed  and
      Weck, Benno  and
      Doh, SeungHeon  and
      Won, Minz  and
      Manco, Ilaria  and
      Meseguer-Brocal, Gabriel",
    booktitle = "Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)",
    month = nov,
    year = "2024",
    address = "Oakland, USA",
    publisher = "Association for Computational Lingustics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.nlp4musa-1.18/",
    pages = "112--118",
    abstract = "Large-scale text-to-music generation models have significantly enhanced music creation capabilities, offering unprecedented creative freedom. However, their ability to collaborate effectively with human musicians remains limited. In this paper, we propose a framework to describe the musical interaction process, which includes expression, interpretation, and execution of controls. Following this framework, we argue that the primary gap between existing text-to-music models and musicians lies in the interpretation stage, where models lack the ability to interpret controls from musicians. We also propose two strategies to address this gap and call on the music information retrieval community to tackle the interpretation challenge to improve human-AI musical collaboration."
}