Positive and Risky Message Assessment for Music Products

• URL: http://arxiv.org/abs/2309.10182v2
• Date: Mon, 8 Apr 2024 07:31:28 GMT
• Title: Positive and Risky Message Assessment for Music Products
• Authors: Yigeng Zhang, Mahsa Shafaei, Fabio A. González, Thamar Solorio,
• Abstract summary: We introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge.
• Score: 9.545182238852545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We initiate by setting a multi-faceted, multi-task benchmark for music content assessment. Subsequently, we introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge. Our findings reveal that the proposed method not only significantly outperforms robust task-specific alternatives but also possesses the capability to assess multiple aspects simultaneously. Furthermore, through detailed case studies, where we employed Large Language Models (LLMs) as surrogates for content assessment, we provide valuable insights to inform and guide future research on this topic. The code for dataset creation and model implementation is publicly available at https://github.com/RiTUAL-UH/music-message-assessment.

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