arXiv cs.AI· Dingming Li, Yiwen Qiu, Linjuan Wu, Hongxing Li, Zixuan Wang, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu, Yongliang Shen·· 4 小时前AI 评分28
Culture-MMT 基准：将视觉证据与文化语用从社交媒体翻译中解耦
Beyond Words Alone: Disentangling Distributed Evidence for Visually Grounded Cultural Translation
AI 导读
研究者推出 Culture-MMT，一个针对社交媒体翻译中“分布式证据语用重构”的诊断基准，包含 498 条去标识 UGC 实例，覆盖 20 个领域，按所需证据分为 Symbol、Visual 和 Hybrid 三类，并配套开发与人类对齐的评测器 Judger。
正文 · 原文
Authors:Dingming Li, Yiwen Qiu, Linjuan Wu, Hongxing Li, Zixuan Wang, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu, Yongliang Shen
Abstract:Social media enables cross-lingual communication, yet the meaning of user-generated content (UGC) is often distributed across text, images, and cultural knowledge, making translation challenging. Existing evaluations typically treat images as optional and assume textual information is sufficient, obscuring whether errors stem from missing visual evidence or inadequate cultural adaptation. We introduce \textbf{Culture-MMT}, a diagnostic benchmark for \textbf{distributed-evidence pragmatic reconstruction} in social-media translation. It contains 498 curated, de-identified UGC instances across 20 domains, categorized into Symbol, Visual, and Hybrid by their required evidence. We also develop a human-aligned domain-specific judge, Judger, and a controlled evaluation framework that systematically varies the evidence available to translators. Across 17 model configurations, real images improve performance by +0.280 on Visual and +0.197 on Hybrid, but only +0.050 on Symbol, while random images provide no comparable gains. Human-authored descriptions further show that jointly dependent cases remain more challenging even when visual facts are explicit. These results characterize culturally effective social-media translation as {meaning reconstruction from distributed evidence}, highlighting visual grounding and cultural-pragmatic adaptation as distinct model bottlenecks.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.35482 [cs.CL] |
| (or arXiv:2610.35482v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35482 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dingming Li [view email]
[v1]
Mon, 26 Oct 2026 14:11:54 UTC (2,783 KB)
来源：arXiv cs.AI · arxiv.org