Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation

• URL: http://arxiv.org/abs/2506.11820v1
• Date: Fri, 13 Jun 2025 14:23:38 GMT
• Title: Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation
• Authors: Xintong Wang, Jingheng Pan, Yixiao Liu, Xiaohu Zhao, Chenyang Lyu, Minghao Wu, Chris Biemann, Longyue Wang, Linlong Xu, Weihua Luo, Kaifu Zhang,
• Abstract summary: Vision-Language Translation is a challenging task that requires accurately recognizing multilingual text embedded in images.<n>We present a comprehensive study of VLT from three key perspectives: data quality, model architecture, and evaluation metrics.
• Score: 45.551223552275424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Translation (VLT) is a challenging task that requires accurately recognizing multilingual text embedded in images and translating it into the target language with the support of visual context. While recent Large Vision-Language Models (LVLMs) have demonstrated strong multilingual and visual understanding capabilities, there is a lack of systematic evaluation and understanding of their performance on VLT. In this work, we present a comprehensive study of VLT from three key perspectives: data quality, model architecture, and evaluation metrics. (1) We identify critical limitations in existing datasets, particularly in semantic and cultural fidelity, and introduce AibTrans -- a multilingual, parallel, human-verified dataset with OCR-corrected annotations. (2) We benchmark 11 commercial LVLMs/LLMs and 6 state-of-the-art open-source models across end-to-end and cascaded architectures, revealing their OCR dependency and contrasting generation versus reasoning behaviors. (3) We propose Density-Aware Evaluation to address metric reliability issues under varying contextual complexity, introducing the DA Score as a more robust measure of translation quality. Building upon these findings, we establish a new evaluation benchmark for VLT. Notably, we observe that fine-tuning LVLMs on high-resource language pairs degrades cross-lingual performance, and we propose a balanced multilingual fine-tuning strategy that effectively adapts LVLMs to VLT without sacrificing their generalization ability.

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