Positive and Risky Message Assessment for Music Products
This addresses the need for content assessment in music products, though it appears incremental as it builds on existing multi-task and ordinality techniques.
The paper tackles the problem of evaluating both positive and potentially harmful messages in music products by creating a multi-task benchmark and developing an efficient multi-task predictive model with ordinality-enforcement. The results show that their method significantly outperforms task-specific alternatives and can assess multiple aspects simultaneously.
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.