| --- |
| license: gpl-3.0 |
| language: |
| - en |
| - zh |
| language_creators: |
| - found |
| - machine-generated |
| task_categories: |
| - translation |
| pretty_name: DoveBench |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - long-form |
| - subtitles |
| - starcraft |
| - video-translation |
| source_datasets: |
| - original |
| --- |
| |
| # DoveBench |
|
|
| A segment-level English→Chinese translation benchmark derived from the ViDove release corpus. DoveBench pairs each Chinese translation segment with an aligned English reference, packaged as a single JSONL so evaluation runs directly without per-file parsing. |
|
|
| Companion dataset for [ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning](https://arxiv.org/abs/2507.07306) (Lu et al., 2025). |
|
|
| ## Quick stats |
|
|
| | | | |
| |---|---| |
| | Videos | **28** | |
| | Segments | **13,704** | |
| | Total audio | **766 min** (12.77 h) | |
| | Language pair | English → Chinese | |
| | Source | sc2 corpus (StarCraft II commentary / interviews) | |
|
|
| > **Note.** Original video files are *not* shipped with this release. Each row carries a `youtube_id`; access the source video at `https://www.youtube.com/watch?v={youtube_id}`. |
|
|
| ### Row schema (`dovebench.jsonl`) |
|
|
| ```json |
| { |
| "video_id": "sc2/27_59", |
| "youtube_id": "G3eZhv5qdDo", |
| "idx": 0, |
| "start": 4.120, |
| "end": 7.840, |
| "zh_text": "...", |
| "en_text": "..." |
| } |
| ``` |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `video_id` | `str` | Corpus-qualified ID (`sc2/<key>`). | |
| | `youtube_id` | `str` | 11-char YouTube video ID — concatenate with `https://www.youtube.com/watch?v=` to view the source. | |
| | `idx` | `int` | Zero-based segment index within the video. | |
| | `start`, `end` | `float` | Segment boundaries in seconds, sourced from the Chinese subtitle timeline. | |
| | `zh_text` | `str` | Chinese translation for this segment. | |
| | `en_text` | `str` | Aligned English reference. | |
|
|
| ## How to use |
|
|
| ### HuggingFace `datasets` |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "json", |
| data_files="dovebench/dovebench.jsonl", |
| split="train", |
| ) |
| row = next(r for r in ds if r["video_id"] == "sc2/13_07") |
| print(row) |
| # → {'video_id': 'sc2/13_07', 'youtube_id': 'n-Dx7MLqFqY', 'idx': 0, |
| # 'start': 0.0, 'end': 3.55, |
| # 'zh_text': '欢迎来到这一场的SC2专业比赛', |
| # 'en_text': 'Welcome back to a professional match of StarCraft 2.'} |
| ``` |
|
|
| ### Plain Python |
|
|
| ```python |
| import json |
| with open("dovebench/dovebench.jsonl") as f: |
| rows = [json.loads(line) for line in f] |
| |
| # Group by video |
| from collections import defaultdict |
| by_video = defaultdict(list) |
| for r in rows: |
| by_video[r["video_id"]].append(r) |
| ``` |
|
|
| ### View a source video |
|
|
| ```python |
| row = rows[0] |
| print(f"https://www.youtube.com/watch?v={row['youtube_id']}&t={int(row['start'])}s") |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{lu2025vidovetranslationagentmultimodal, |
| title={ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning}, |
| author={Yichen Lu and Wei Dai and Jiaen Liu and Ching Wing Kwok and Zongheng Wu and Xudong Xiao and Ao Sun and Sheng Fu and Jianyuan Zhan and Yian Wang and Takatomo Saito and Sicheng Lai}, |
| year={2025}, |
| eprint={2507.07306}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2507.07306}, |
| } |
| ``` |
|
|
