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    <abstract>Vulnerability to lexical perturbation is a critical weakness of automatic evaluation metrics for image captioning. This paper proposes Perturbation Robust Multi-Lingual CLIPScore(PR-MCS), which exhibits robustness to such perturbations, as a novel reference-free image captioning metric applicable to multiple languages. To achieve perturbation robustness, we fine-tune the text encoder of CLIP with our language-agnostic method to distinguish the perturbed text from the original text. To verify the robustness of PR-MCS, we introduce a new fine-grained evaluation dataset consisting of detailed captions, critical objects, and the relationships between the objects for 3,000 images in five languages. In our experiments, PR-MCS significantly outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages, while maintaining a strong correlation with human judgments.</abstract>
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%0 Conference Proceedings
%T PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning
%A Kim, Yongil
%A Hwang, Yerin
%A Yun, Hyeongu
%A Yoon, Seunghyun
%A Bui, Trung
%A Jung, Kyomin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kim-etal-2023-pr
%X Vulnerability to lexical perturbation is a critical weakness of automatic evaluation metrics for image captioning. This paper proposes Perturbation Robust Multi-Lingual CLIPScore(PR-MCS), which exhibits robustness to such perturbations, as a novel reference-free image captioning metric applicable to multiple languages. To achieve perturbation robustness, we fine-tune the text encoder of CLIP with our language-agnostic method to distinguish the perturbed text from the original text. To verify the robustness of PR-MCS, we introduce a new fine-grained evaluation dataset consisting of detailed captions, critical objects, and the relationships between the objects for 3,000 images in five languages. In our experiments, PR-MCS significantly outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages, while maintaining a strong correlation with human judgments.
%R 10.18653/v1/2023.findings-emnlp.819
%U https://aclanthology.org/2023.findings-emnlp.819
%U https://doi.org/10.18653/v1/2023.findings-emnlp.819
%P 12237-12258

Markdown (Informal)

[PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning](https://aclanthology.org/2023.findings-emnlp.819) (Kim et al., Findings 2023)

• PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning (Kim et al., Findings 2023)

ACL

• Yongil Kim, Yerin Hwang, Hyeongu Yun, Seunghyun Yoon, Trung Bui, and Kyomin Jung. 2023. PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12237–12258, Singapore. Association for Computational Linguistics.