CaMMT：面向文化感知的多模态机器翻译基准测试
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
AI总结：
本文提出人工整理的基准 CaMMT，包含超 5,800 组图像与英—地区语言平行字幕，用于评估五个视觉语言模型，发现视觉语境能提升文化特定项处理、消歧和性别标记等翻译质量。
摘要：
由于文化之间概念化方式的差异，翻译文化内容对机器翻译系统构成挑战，仅靠语言往往无法传达足够的上下文来捕捉特定地区的含义。在本工作中，我们研究图像是否能在多模态翻译中充当文化语境。我们引入了 CaMMT，这是一个由人工整理的基准，包含超过 5,800 组三元数据，每组由图像以及英语和地区语言的平行字幕组成。利用该数据集，我们在纯文本和文本+图像两种设置下评估了五个视觉语言模型（VLMs）。通过自动评估和人工评估，我们发现视觉语境总体上能提升翻译质量，尤其是在处理文化特定项（CSIs）、消歧以及正确的性别标记方面。通过发布 CaMMT，我们的目标是支持更广泛的努力，以构建和评估更贴合文化细微差别和地区差异的多模态翻译系统。
英文摘要：
Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
