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        <title>X-ACE: Explainable and Multi-factor Audio Captioning Evaluation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T X-ACE: Explainable and Multi-factor Audio Captioning Evaluation
%A Wang, Qian
%A Gu, Jia-Chen
%A Ling, Zhen-Hua
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wang-etal-2024-x
%X 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.
%U https://aclanthology.org/2024.findings-acl.729
%P 12273-12287

Markdown (Informal)

[X-ACE: Explainable and Multi-factor Audio Captioning Evaluation](https://aclanthology.org/2024.findings-acl.729) (Wang et al., Findings 2024)

• X-ACE: Explainable and Multi-factor Audio Captioning Evaluation (Wang et al., Findings 2024)

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.