Abstract
Automated audio captioning (AAC) aims to generate descriptions based on audio input, attracting exploration of emerging audio language models (ALMs). However, current evaluation metrics only provide a single score to assess the overall quality of captions without characterizing the nuanced difference by systematically going through an evaluation checklist. To this end, we propose the explainable and multi-factor audio captioning evaluation (X-ACE) paradigm. X-ACE identifies four main factors that constitute the majority of audio features, specifically sound event, source, attribute and relation. To assess a given caption from an ALM, it is firstly transformed into an audio graph, where each node denotes an entity in the caption and corresponds to a factor. On the one hand, graph matching is conducted from part to whole for a holistic assessment. On the other hand, the nodes contained within each factor are aggregated to measure the factor-level performance. The pros and cons of an ALM can be explicitly and clearly demonstrated through X-ACE, pointing out the direction for further improvements. Experiments show that X-ACE exhibits better correlation with human perception and can detect mismatches sensitively.- Anthology ID:
- 2024.findings-acl.729
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12273–12287
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.729
- DOI:
- Cite (ACL):
- Qian Wang, Jia-Chen Gu, and Zhen-Hua Ling. 2024. X-ACE: Explainable and Multi-factor Audio Captioning Evaluation. In Findings of the Association for Computational Linguistics ACL 2024, pages 12273–12287, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- X-ACE: Explainable and Multi-factor Audio Captioning Evaluation (Wang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.729.pdf