README.md

---
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

<!-- Provide a quick summary of the dataset. -->

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

<!-- Provide the basic links for the dataset. -->

- **Paper:** [CAMMT: Benchmarking Culturally Aware Multimodal Machine Translation](https://arxiv.org/abs/2505.24456)

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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}, 
}