zh_translation_benchmark

zh_translation_benchmark is a 2,000-example synthetic benchmark for evaluating whether a translation or rewriting model can produce natural Taiwan Traditional Chinese (zh-TW) from English, Mainland Chinese, Hong Kong Traditional Chinese, Cantonese-style written Chinese, or code-mixed English/Chinese documents.

The dataset now exposes a single Hugging Face subset/config: full. It is not split into dev and test files. The full 2,000 rows are loaded together from data/full.jsonl. The original row-level split column is kept as metadata for downstream analysis, but the Hub dataset itself is published as one complete subset.

Credits

This dataset is created and released by OpenFormosa.

Loading

from datasets import load_dataset

dataset = load_dataset("OpenFormosa/zh_translation_benchmark", "full")

# Hugging Face stores a single data file under the conventional "train" split.
full = dataset["train"]
print(full.num_rows)  # 2000
print(full[0])

If you want a plain Dataset object:

from datasets import load_dataset

full = load_dataset("OpenFormosa/zh_translation_benchmark", "full", split="train")

Version Summary

This v2 release merges the earlier v0 sentence-level localization set with a v1 long-document and code-mixed localization set.

• v0: sentence-level locale-sensitive translation from English, zh-CN, and zh-Hant-HK into Taiwan Traditional Chinese.
• v1: paragraph and long-document localization with code-mixing, Markdown, placeholders, cross-paragraph terminology consistency, and broader domain coverage.
• v2: unified schema with one official full subset containing all 2,000 rows.

Dataset Summary

Property	Value
Rows	2,000
Unique dedup hashes	2,000
Domains	56
Domain groups	18
Source versions	v0: 1,000; v1: 1,000
Published subset/config	full
Published split files	none; one full JSONL file

Direction Distribution

Direction	Count
English -> Taiwan Traditional Chinese	950
zh-CN -> Taiwan Traditional Chinese	550
mixed English/Chinese -> Taiwan Traditional Chinese	300
zh-Hant-HK -> Taiwan Traditional Chinese	200

Length Distribution

Length bucket	Count
Sentence	1,500
Paragraph	300
Long document	200

What This Dataset Tests

Many systems can output Traditional Chinese characters but still leave Mainland Chinese, Hong Kong wording, or translated English workplace jargon in the sentence. This benchmark targets those errors directly.

Examples of intended localization behavior:

Non-Taiwan or source term	Preferred Taiwan-localized handling
用户	使用者
默认	預設
缓存	快取
服务器	伺服器
信息	資訊
数据	資料
地铁	捷運 in Taiwan contexts
公交	公車
出租车 / 的士	計程車
外卖	外送
身份证 / 身份證	身分證
医保卡	健保卡
港鐵 in Taiwan-localized transport copy	捷運
八達通 in Taiwan-localized app copy	悠遊卡

v2 also tests:

• Cross-paragraph terminology consistency
• Markdown and placeholder preservation
• Code-mixed workplace text
• UI and product copy localization
• Public-service, medical, legal, finance, and customer-support register
• Whether English terms should be retained or translated in Taiwan contexts

Domains

The full set covers 56 domains across 18 domain groups. Major domains include:

Domain	Count
software_it	130
government_public_service	72
workplace_business	67
education	66
ecommerce_product_ui	61
zhcn_software_it	60
zhcn_ecommerce	50
zhcn_workplace	50
zhcn_public_health_gov	50
zhcn_daily_transit_food	50
zhcn_education_media	40
product_ui	37
cybersecurity	37
ecommerce	37
fintech_banking	37
insurance	37
healthcare_clinic	37
healthcare	36
marketing_social_media	35
medical_device	35
legal_privacy	35
academic_research	35
workplace_hr	35

Use the domain, domain_group, domain_zh, and subdomain columns for more detailed breakdowns.

Files

Path	Description
data/full.jsonl	Single 2,000-row JSONL file used by the Hugging Face loader
data/full.csv	Single 2,000-row CSV file
full/tw_localization_benchmark_v2_2000.csv	Complete 2,000-row CSV
full/tw_localization_benchmark_v2_2000.jsonl	Complete 2,000-row JSONL
full/tw_localization_benchmark_v2.xlsx	Workbook with Overview, Examples_2000, Core_1000, Rubric, Locale_Policy, Glossary, Eval_Prompts, Auto_Checks, Model_Slots, and ChangeLog sheets
full/tw_localization_benchmark_v2_manifest.json	Manifest with counts, checksums, schema, and historical core selection rule
full/tw_localization_benchmark_v2_README.md	Original v2 local package README

Schema

Each example contains:

Field	Description
id	Stable v2 ID, such as TWLOC-V2-0001
legacy_id	Original v0 or v1 ID
version_source	Source version: v0 or v1
core_1000	Whether the row belonged to the historical core set
split	Original dev/test marker retained as row metadata
source_lang	Normalized source language bucket
source_locale_raw	Original source locale label
target_lang	Target language, zh-TW
direction	Translation or rewriting direction
domain_group	High-level domain group
domain	Domain label
domain_zh	Chinese domain label
subdomain	Optional subdomain
genre	Genre or text style
doc_type	Document type
length_bucket	sentence, paragraph, or long_doc
source_len_chars	Source length in characters
target_len_chars	Reference length in characters
code_mix_level	Code-mixing level
code_mix_ratio_est	Estimated code-mix ratio
code_mix_pattern	Code-mixing pattern
context_dependency	Required cross-segment context, if any
phenomenon	Main evaluated phenomenon
phenomena_tags	Additional phenomenon tags
difficulty	Estimated difficulty from 1 to 5
stress_axes	Main stress-test axis
source_text	Input text
reference_tw_zh	Reference Taiwan Traditional Chinese output
acceptable_variants	Optional acceptable alternate wording
must_preserve	Terms, placeholders, URLs, Markdown, numbers, names, or formatting that must be preserved
avoid_terms	Row-level Mainland, Hong Kong, or unnatural terms to avoid
locale_policy	Taiwan localization policy for the row
eval_notes	Evaluation notes
comment	Optional comment
data_origin	Data provenance marker
review_status	Current review status
dedup_hash	Deduplication hash
release_tag	Release tag
created_date	Creation date

Example

{
  "id": "TWLOC-V2-0001",
  "legacy_id": "TWLOC-0001",
  "version_source": "v0",
  "core_1000": "TRUE",
  "split": "dev",
  "source_lang": "en",
  "source_locale_raw": "en-US",
  "target_lang": "zh-TW",
  "direction": "en→zh-TW",
  "domain_group": "technology",
  "domain": "software_it",
  "domain_zh": "軟體與資訊技術",
  "length_bucket": "sentence",
  "source_text": "Users reported that the admin console keeps spinning after the latest release.",
  "reference_tw_zh": "使用者回報，最新版本釋出後，後台管理介面一直卡在載入中。",
  "avoid_terms": "用户、发布、一直转圈",
  "release_tag": "TW-Local-MT-Bench-v2"
}

Recommended Inference Prompt

請將以下文字或文件翻譯或轉寫成台灣本土慣用的繁體中文。請保留原文的事實、專有名詞、數字、日期、貨幣、Markdown、HTML tag、placeholder、URL、email、SLA、KPI、API 名稱與格式。若原文中英文夾雜，請判斷哪些英文是產品名、程式介面、檔名、欄位名或常見縮寫而應保留，哪些只是英文工作口語而應翻成自然台灣中文。請保持全文術語一致，避免中國大陸或香港用語。只輸出譯文。

來源文字：{source_text}

Evaluation

Suggested 100-point human evaluation rubric:

Dimension	Weight	What to check
Adequacy	30	Source facts, conditions, negation, numbers, entities, and causality
Taiwan localization	25	Taiwan vocabulary, institutions, tone, punctuation, and document style
Terminology consistency	15	Consistent terminology across paragraphs and documents
Code-mix handling	10	Correctly keeping product/API/file/field names while translating casual English into natural Taiwan Chinese
Fluency and register	10	Natural, readable output matching customer support, legal, medical, UI, government, or casual register
Format and constraint preservation	10	Markdown, placeholders, URLs, HTML, numbers, currencies, tables, and bullets

LLM-Assisted Judging Prompt

你是台灣繁體中文翻譯評審。請比較來源文字、參考譯文、模型輸出。請輸出 JSON：adequacy_0_30、tw_localization_0_25、terminology_consistency_0_15、code_mix_handling_0_10、fluency_register_0_10、format_constraints_0_10、total_0_100、major_errors、minor_errors、error_spans、comment。請優先懲罰語意錯誤、簡體字、中國大陸或香港用語、台灣制度名詞錯誤、術語前後不一致、Markdown/placeholder/URL/數字或專有名詞改錯。

Intended Uses

This dataset is suitable for:

• Taiwan-localized machine translation evaluation
• Prompt testing for zh-TW translation systems
• Comparing open-weight or open-source LLMs on Taiwan localization
• Measuring Mainland/Hong Kong terminology leakage in Traditional Chinese output
• Testing code-mixed and long-document localization
• Building human annotation workflows and internal model regression tests
• Stress-testing Markdown, placeholder, URL, and terminology preservation

Out-of-Scope Uses

This dataset is not intended to be:

• A complete public leaderboard without an additional private hidden test set
• A substitute for professional legal, medical, or government translation review
• A general-purpose Traditional Chinese corpus
• Evidence that one model is broadly better at all Chinese-language tasks
• A source of real user data

Data Creation and Review Notes

This is a synthetic benchmark intended for evaluation pipeline development, preliminary model comparison, error analysis, and rubric design.

The current review marker is:

needs_native_taiwanese_reviewer_pass_before_public_release

For a public leaderboard, the recommended process is:

1. Add a Taiwan-native reviewer pass before freezing the references.
2. Keep an additional private hidden test set.
3. Do not rely on a single reference-matching metric as the final ranking criterion.

Checksums

Checksums for the v2 release files are recorded in full/tw_localization_benchmark_v2_manifest.json.

Citation

@dataset{openformosa_zh_translation_benchmark_2026,
  title = {zh_translation_benchmark},
  author = {OpenFormosa},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/OpenFormosa/zh_translation_benchmark}
}

License

Released under the Creative Commons Attribution 4.0 International license (cc-by-4.0) unless superseded by a later repository-level license notice.