CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
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 usage. By releasing CaMMT, we aim to support broader efforts in building and evaluating multimodal translation systems that are better aligned with cultural nuance and regional variation.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingMachine TranslationMultimodal Machine TranslationTranslationSimilar Papers 제목 키워드 기반
TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
Recent progress in Multimodal Large Language Models (MLLMs) have significantly enhanced the ability of artificial intelligence systems to understand and generate multimodal content. However, these models often exhibit li…
BenchmarkingQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs
Vision-language models (VLMs) have demonstrated impressive generalization across multimodal tasks, yet most evaluation benchmarks remain Western-centric, leaving open questions about their performance in culturally diver…
Multimodal Machine TranslationVisual Question AnsweringSurvey of Cultural Awareness in Language Models: Text and Beyond
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely…
BenchmarkingBenchmarking Machine Translation with Cultural Awareness
Translating culture-related content is vital for effective cross-cultural communication. However, many culture-specific items (CSIs) often lack viable translations across languages, making it challenging to collect high-…
BenchmarkingIn-Context LearningMachine TranslationNMT+1Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs
The rapid evolution of multimodal large language models (MLLMs) has significantly enhanced their real-world applications. However, achieving consistent performance across languages, especially when integrating cultural k…
BenchmarkingQuestion AnsweringVisual Question Answering