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Positive and Risky Message Assessment for Music Products

Yigeng Zhang, Mahsa Shafaei, Fabio A. González, Thamar Solorio

TL;DR

This work defines a novel task for assessing both positive and potentially risky messages in music lyrics and builds a multi-task benchmark across five aspects with ordinal severity. It introduces an emotion-guided twin encoder and an aspect-aware attention mechanism, augmented with three ordinality-enforcement strategies, to jointly predict all five aspects. Empirical results show state-of-the-art performance on the benchmark, with thorough analyses including ablation, saliency, error analysis, case studies, and a case study using an LLM as a surrogate evaluator. The work provides public code and data procedures, highlighting practical implications for music content advisories, and discusses ethical and societal considerations for automated content assessment.

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.

Positive and Risky Message Assessment for Music Products

TL;DR

This work defines a novel task for assessing both positive and potentially risky messages in music lyrics and builds a multi-task benchmark across five aspects with ordinal severity. It introduces an emotion-guided twin encoder and an aspect-aware attention mechanism, augmented with three ordinality-enforcement strategies, to jointly predict all five aspects. Empirical results show state-of-the-art performance on the benchmark, with thorough analyses including ablation, saliency, error analysis, case studies, and a case study using an LLM as a surrogate evaluator. The work provides public code and data procedures, highlighting practical implications for music content advisories, and discusses ethical and societal considerations for automated content assessment.

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.
Paper Structure (18 sections, 1 equation, 7 figures, 6 tables)

This paper contains 18 sections, 1 equation, 7 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Music rating and lyrics
  3. Dataset facts and analysis
  4. Inter-dimensional correlation analysis
  5. Positive/Risky behavior v.s. Explicitness
  6. Methodology
  7. Experiments and Results
  8. Discussion and analysis
  9. Ablation study
  10. Saliency analysis
  11. Error analysis
  12. Case study: unsuccessful predictions
  13. Case study: LLM as content judges
  14. Conclusion
  15. Bibliographical References
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Lyrics length distribution for CD singles and albums.
  • Figure 2: Spearman rank correlation between positive and risky prevalence pairs.
  • Figure 3: Explicitness probability on different message dimensions. 20 instances are randomly sampled from each level. The diamond symbol $\medblackdiamond$ indicates the central tendency of a series of probability values for the corresponding level.
  • Figure 4: Joint prediction architecture with emotion-guided twin and aspect-aware attention module.
  • Figure 5: Prediction confusion matrix of the best-performing method on all 5 aspects. The x-axis indicates the predicted values and the y-axis indicates the ground-truth labels.
  • ...and 2 more figures