CULTURE-MT: Beyond Literal Translation — Evaluating Cultural Effectiveness in Social Media UGC
CULTURE-MT is a benchmark for evaluating CULtural Transmission and UGC-specific emotion REsonance in Chinese-to-English social media translation. It consists of 1,002 user-generated notes (UGC) spanning 14 content domains, presented at ICML 2026.
📄 Paper: Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC
🌟 Why CULTURE-MT?
Standard machine translation metrics (BLEU, ChrF, COMET) fail to capture whether a translation truly resonates with target-language users. CULTURE-MT introduces cultural effectiveness as a new evaluation criterion, covering:
- Expressive accuracy — semantic fidelity, emotional tone, proper noun handling, and unit/measurement accuracy
- Cultural adaptability — culture-loaded term handling, overall cultural fluency, and addressing/politeness adaptation
📊 Dataset
| Split | Notes | Domains | Note Types |
|---|---|---|---|
| Benchmark | 1,002 | 14 | 4 (General, Express, Symbol, Hybrid) |
Content Domains
Pets · Travel · Food · Crafts · Painting · Home Decoration · Outdoor · Sports · Fitness & Weight Loss · Technology & Gadgets · Cars · Games · Movies & TV · Celebrity News
Note Types
| Type | Description |
|---|---|
| General | Informal UGC with few culture-loaded symbols or distinctive styles |
| Express | Strong rhetorical/expressive style (e.g., "planting grass", hyperbole, rhetorical questions) |
| Symbol | High density of internet cultural symbols (slang, memes, platform-specific jargon) |
| Hybrid | Both rich cultural symbols and distinctive expressive style |
📐 Evaluation
Translations are evaluated by JUDGER, a fine-tuned Qwen3-32B model trained on 30K expert- and LLM-annotated samples. It achieves 86.03% accuracy and Cohen's κ = 0.72 against human expert judgments.
Scoring Rubric (0–3 scale)
| Score | Meaning |
|---|---|
| 0 | Severe meaning loss or distortion; target readers cannot grasp the original intent or emotion |
| 1 | Main idea barely understandable; critical cultural errors, poor adaptation |
| 2 | Main information conveyed accurately; reasonable emotional/contextual expression |
| 3 | Precise, natural, culturally fluent; fully conveys all information and emotion for English social media readers |
Scores 0–1 are treated as culturally ineffective; scores 2–3 as culturally effective.
🏆 Leaderboard
Submit your translations and get evaluated automatically by our trained JUDGER model at: 👉 https://huggingface.co/spaces/Wulinjuan/CULTURE-MT
📌 Note on Leaderboard vs. Paper Results Scores reported in the ICML 2026 paper were produced with a single JUDGER inference pass (temperature = 0.6, top-p = 0.95). Since stochastic decoding introduces minor variation across runs, leaderboard scores are computed as the average of four independent inference passes under identical settings to ensure fairness and reproducibility. As a result, leaderboard scores may differ slightly from those reported in the paper.
Top Results (as of 2026-05-27)
| Rank | Model | Ineff. ↓ | Eff. ↑ | 0 ↓ | 1 ↓ | 2 | 3 | Avg Score |
|---|---|---|---|---|---|---|---|---|
| 1 | GPT-5 | 8.08% | 91.92% | 0.90% | 7.19% | 51.56% | 40.35% | 2.31 |
| 2 | Gemini 3 Pro | 8.95% | 91.05% | 0.20% | 8.75% | 51.97% | 39.09% | 2.30 |
| 3 | CULTURE-MT-baseline-32B | 12.08% | 87.92% | 0.73% | 11.35% | 56.56% | 31.37% | 2.19 |
| 4 | CULTURE-MT-baseline-8B | 14.34% | 85.66% | 1.27% | 13.07% | 57.42% | 28.24% | 2.13 |
| 5 | GLM4.6 | 15.94% | 84.06% | 3.12% | 12.81% | 57.39% | 26.68% | 2.08 |
| 6 | DeepSeek V3.2 | 17.73% | 82.27% | 2.59% | 15.14% | 56.82% | 25.45% | 2.05 |
| 7 | Qwen3-235B-A22B | 29.27% | 70.73% | 12.31% | 16.97% | 52.86% | 17.87% | 1.76 |
| 8 | Seed-X-PPO | 30.58% | 69.42% | 3.43% | 27.25% | 62.23% | 7.19% | 1.73 |
📤 How to Submit
Submissions are made directly through the leaderboard interface at: 👉 https://huggingface.co/spaces/Wulinjuan/CULTURE-MT
Step 1 — Upload your translations
Upload a submission.jsonl file. Each line should be a JSON object with the id of the source note and your translation:
{"id": "0001", "translation": "This place is amazing. I'll definitely come back!"}
{"id": "0002", "translation": "This is way too ridiculous."}
Step 2 — Fill in model information
Complete the submission form with your model details (Model Name, Organization / Team, Base Model, Method, and a brief description). No additional files are required.
Step 3 — Get your results
Aggregated scores will appear on the leaderboard automatically after evaluation. If you need detailed per-sample evaluation results, please contact us by email at wulinjuan525@zju.edu.cn.
📖 Citation
If you use CULTURE-MT in your research, please cite:
@misc{wu2026literaltranslationevaluatingcultural,
title={Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC},
author={Linjuan Wu and Ruiqi Zhang and Xinze Lyu and Ye Guo and Daoxin Zhang and Zhe Xu and Yao Hu and Yixin Cao and Yongliang Shen and Weiming Lu},
year={2026},
eprint={2605.25626},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2605.25626},
}
📬 Contact
- Linjuan Wu: wulinjuan525@zju.edu.cn
This benchmark was developed with support from Zhejiang University, Fudan University, and Xiaohongshu Inc.