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.

While Marathi is considered as a low- to middle-resource language, its 42 dialects have mostly been ignored, mainly because these dialects are mostly spoken and rarely written, making them extremely low-resource. In this paper we explore the machine translation (MT) of Kadodi, also known as Samvedi, which is a dialect of Marathi. We first discuss the Kadodi dialect, highlighting the differences from the standard dialect, followed by presenting a manually curated dataset called Suman consisting of a trilingual Kadodi-Marathi-English dictionary of 949 entries and 942 simple sentence triples and idioms created by native Kadodi speakers. We then evaluate 3 existing large language models (LLMs) supporting Marathi, namely Gemma-2-9b, Sarvam-2b-0.5 and LLaMa-3.1-8b, in few-shot prompting style to determine their efficacy for translation involving Kadodi. We observe that these models exhibit rather lackluster performance in handling Kadodi even for simple sentences, indicating a dire situation.