--- dataset_info: features: - name: ID dtype: string - name: regional dtype: string - name: English dtype: string - name: Conserved_translation dtype: string - name: Substituted_translation dtype: string - name: Category dtype: string - name: Preferred_translation dtype: string - name: image dtype: image splits: - name: es_mex num_bytes: 158368543.0 num_examples: 323 - name: bn_india num_bytes: 94017886.0 num_examples: 286 - name: om_eth num_bytes: 28490930.0 num_examples: 214 - name: ur_india num_bytes: 102386298.0 num_examples: 220 - name: ig_nga num_bytes: 14372042.0 num_examples: 200 - name: ur_pak num_bytes: 147129846.0 num_examples: 216 - name: zh_ch num_bytes: 91877910.0 num_examples: 308 - name: es_ecu num_bytes: 141969979.0 num_examples: 362 - name: sw_ken num_bytes: 31567516.0 num_examples: 271 - name: kor_sk num_bytes: 143897056.0 num_examples: 290 - name: ru_rus num_bytes: 56598710.0 num_examples: 200 - name: ta_india num_bytes: 142254878.0 num_examples: 213 - name: amh_eth num_bytes: 122937506.0 num_examples: 234 - name: jp_jap num_bytes: 63884062.0 num_examples: 203 - name: fil_phl num_bytes: 42171387.0 num_examples: 203 - name: ms_mys num_bytes: 84408174.0 num_examples: 315 - name: bg_bg num_bytes: 179103702.0 num_examples: 369 - name: es_chl num_bytes: 98202963.0 num_examples: 234 - name: pt_brz num_bytes: 214095076.0 num_examples: 284 - name: ar_egy num_bytes: 106134417.0 num_examples: 203 - name: ind_ind num_bytes: 116476184.0 num_examples: 202 - name: mr_india num_bytes: 145040535.0 num_examples: 202 - name: es_arg num_bytes: 142144959.0 num_examples: 265 download_size: 1952703427 dataset_size: 2467530559.0 configs: - config_name: default data_files: - split: es_mex path: data/es_mex-* - split: bn_india path: data/bn_india-* - split: om_eth path: data/om_eth-* - split: ur_india path: data/ur_india-* - split: ig_nga path: data/ig_nga-* - split: ur_pak path: data/ur_pak-* - split: zh_ch path: data/zh_ch-* - split: es_ecu path: data/es_ecu-* - split: sw_ken path: data/sw_ken-* - split: kor_sk path: data/kor_sk-* - split: ru_rus path: data/ru_rus-* - split: ta_india path: data/ta_india-* - split: amh_eth path: data/amh_eth-* - split: jp_jap path: data/jp_jap-* - split: fil_phl path: data/fil_phl-* - split: ms_mys path: data/ms_mys-* - split: bg_bg path: data/bg_bg-* - split: es_chl path: data/es_chl-* - split: pt_brz path: data/pt_brz-* - split: ar_egy path: data/ar_egy-* - split: ind_ind path: data/ind_ind-* - split: mr_india path: data/mr_india-* - split: es_arg path: data/es_arg-* --- # CaMMT Dataset Card CaMMT is a human-curated benchmark dataset for evaluating multimodal machine translation systems on culturally-relevant content. The dataset contains over 5,800 image-caption triples across 19 languages and 23 regions, with parallel captions in English and regional languages, specifically designed to assess how visual context impacts translation of culturally-specific items. ```python from datasets import load_dataset # Load the full dataset dataset = load_dataset("villacu/cammt") # Load a specific split if available dataset = load_dataset("villacu/cammt", split="ar_egy") ``` ## Dataset Details ### Dataset Description CAMMT addresses the challenge of translating cultural content by investigating whether images can serve as cultural context in multimodal translation. The dataset is built upon the CVQA (Culturally-diverse multilingual Visual Question Answering) dataset, transforming question-answer pairs into declarative caption statements. Each entry includes parallel captions in English and regional languages, with special attention to Culturally-Specific Items (CSIs) and their translation strategies. The dataset includes both conserved translations (preserving original cultural terms) and substituted translations (using familiar equivalents) for items containing CSIs, along with native speaker preferences for translation strategies. - **Curated by:** MBZUAI and collaborating institutions across the globe. - **Language(s) (NLP):** 19 languages across 23 regions (Amharic, Arabic, Bengali, Bulgarian, Chinese, Filipino, Igbo, Indonesian, Japanese, Korean, Malay, Marathi, Oromo, Portuguese, Russian, Spanish (4 regional variants), Swahili, Tamil, Urdu (2 regional variants)) - **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ### Dataset Sources - **Paper:** [CAMMT: Benchmarking Culturally Aware Multimodal Machine Translation](https://arxiv.org/abs/2505.24456) ## Dataset Structure The dataset contains 5,817 main entries plus an additional 1,550 entries with conserved and substituted CSI translations. Each entry includes: - **ID**: Unique identifier from the original CVQA dataset - **regional**: Caption in the regional language - **English**: Parallel caption in English - **Conserved_translation**: English translation preserving the original CSI (if applicable) - **Substituted_translation**: English translation using a familiar equivalent for the CSI (if applicable) - **Category**: Classification of cultural relevance: - `"not culturally-relevant sentence"` - `"non-CSI"` (culturally relevant but no specific CSI) - `"CSI- has possible translation"` (CSI with cultural equivalent) - `"CSI-forced translation"` (CSI without direct equivalent) - **Preferred_translation**: Native speaker preference between conserved or substituted translation (if applicable) The dataset spans 23 regions with varying numbers of samples per region (ranging from 200 to 369 samples). ## Citation **BibTeX:** ```bibtex @misc{villacueva2025cammtbenchmarkingculturallyaware, title={CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation}, author={Emilio Villa-Cueva and Sholpan Bolatzhanova and Diana Turmakhan and Kareem Elzeky and Henok Biadglign Ademtew and Alham Fikri Aji and Israel Abebe Azime and Jinheon Baek and Frederico Belcavello and Fermin Cristobal and Jan Christian Blaise Cruz and Mary Dabre and Raj Dabre and Toqeer Ehsan and Naome A Etori and Fauzan Farooqui and Jiahui Geng and Guido Ivetta and Thanmay Jayakumar and Soyeong Jeong and Zheng Wei Lim and Aishik Mandal and Sofia Martinelli and Mihail Minkov Mihaylov and Daniil Orel and Aniket Pramanick and Sukannya Purkayastha and Israfel Salazar and Haiyue Song and Tiago Timponi Torrent and Debela Desalegn Yadeta and Injy Hamed and Atnafu Lambebo Tonja and Thamar Solorio}, year={2025}, eprint={2505.24456}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.24456}, }