MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng
Abstract
Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese. Despite pioneering works expanding multilingual QA pairs in non-text-centric VQA datasets through translation engines, the translation-based protocol encounters a substantial “visual-textual misalignment” problem when applied to TEC-VQA. Specifically, it prioritizes the text in question-answer pairs while disregarding the visual text present in images. Moreover, it fails to address complexities related to nuanced meaning, contextual distortion, language bias, and question-type diversity. In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. Further, by comprehensively evaluating numerous state-of-the-art Multimodal Large Language Models (MLLMs), including Qwen2.5-VL, InternVL-2.5, GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA benchmark, it is evident that there is still a large room for performance improvement (InternVL-2.5 scoring 32.2 versus 79.7 for human performance), underscoring the value of MTVQA. By providing a dataset with nuanced multilingual annotations, MTVQA aims to set a new standard for benchmarks, fostering advancements in multilingual visual text comprehension.
BibTeX
@inproceedings{tang-etal-2025-mtvqa,
title = "{MTVQA}: Benchmarking Multilingual Text-Centric Visual Question Answering",
author = "Tang, Jingqun and
Liu, Qi and
Ye, Yongjie and
Lu, Jinghui and
Wei, Shu and
Wang, An-Lan and
Lin, Chunhui and
Feng, Hao and
Zhao, Zhen and
Wang, Yanjie and
Liu, Yuliang and
Liu, Hao and
Bai, Xiang and
Huang, Can",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.404/",
doi = "10.18653/v1/2025.findings-acl.404",
pages = "7748--7763",
ISBN = "979-8-89176-256-5"
}