April 9, 2024, 4:51 a.m. | Yigeng Zhang, Mahsa Shafaei, Fabio A. Gonz\'alez, Thamar Solorio

cs.CL updates on arXiv.org arxiv.org

arXiv:2309.10182v2 Announce Type: replace
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 …

arxiv assessment cs.ai cs.cl music positive products type

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