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Strategies for translating academic papers while preserving technical accuracy
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npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill multilingual-research-guideコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
SKILL.md を表示中
SOC 職業分類に基づく
| name | multilingual-research-guide |
| description | Strategies for translating academic papers while preserving technical accuracy |
| metadata | {"openclaw":{"emoji":"🌐","category":"tools","subcategory":"ocr-translate","keywords":["translation strategies","document OCR","math OCR","academic writing","multilingual research"],"source":"wentor"}} |
A skill for translating academic papers, theses, and research documents between languages while preserving technical precision, citation integrity, and discipline-specific terminology. Covers workflow design, terminology management, and quality assurance.
Source Document
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v
1. Document Preparation
- Extract text (OCR if scanned)
- Identify formulas, figures, tables (do NOT translate these)
- Build terminology glossary
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2. Segmentation
- Split into translatable units (sentences/paragraphs)
- Tag non-translatable elements: equations, citations, proper nouns
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3. Translation
- Apply machine translation (first pass)
- Human post-editing (second pass)
- Terminology consistency check (third pass)
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4. Quality Assurance
- Back-translation verification (sample)
- Domain expert review
- Formatting and citation check
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Target Document
import json
def build_terminology_glossary(source_text: str, domain: str,
source_lang: str = ,
target_lang: = ) -> []:
glossaries = {
: {
: {
: ,
: ,
: ,
: ,
: ,
: ,
: ,
: ,
: ,
:
}
},
: {
: {
: ,
: ,
: ,
: ,
: ,
:
}
}
}
domain_terms = glossaries.get(domain, {}).get(, {})
entries = []
source_term, target_term domain_terms.items():
source_term source_text:
entries.append({
: source_term,
: target_term,
: domain,
: ,
:
})
entries
def enforce_terminology(translated_text: str,
glossary: list[dict]) -> tuple[str, list[str]]:
"""
Check and enforce terminology consistency in translated text.
Returns:
Tuple of (corrected_text, list of warnings)
"""
warnings = []
corrected = translated_text
for entry in glossary:
target_term = entry['target']
# Check for common mistranslations or inconsistent usage
variants = entry.get('incorrect_variants', [])
for variant in variants:
if variant.lower() in corrected.lower():
warnings.append(
f"Found '{variant}' -- should be '{target_term}'"
)
# Case-insensitive replacement
import re
corrected = re.sub(
re.escape(variant), target_term, corrected,
flags=re.IGNORECASE
)
return corrected, warnings
import deepl
def translate_academic_text(text: str, source_lang: str, target_lang: str,
auth_key: str, glossary_id: str = None) -> str:
"""
Translate academic text using DeepL with optional glossary.
"""
translator = deepl.Translator(auth_key)
result = translator.translate_text(
text,
source_lang=source_lang.upper(),
target_lang=target_lang.upper(),
formality="more", # academic style
glossary=glossary_id,
preserve_formatting=True,
tag_handling="xml" # preserve XML/HTML tags
)
return result.text
Before sending text to any translation engine, protect elements that should not be translated:
import re
def protect_elements(text: str) -> tuple[str, dict]:
"""
Replace non-translatable elements with placeholders.
Returns protected text and a mapping to restore later.
"""
placeholders = {}
counter = 0
# Protect LaTeX equations
for pattern in [r'\$\$.*?\$\$', r'\$.*?\$', r'\\begin\{equation\}.*?\\end\{equation\}']:
for match in re.finditer(pattern, text, re.DOTALL):
key = f'__MATH_{counter}__'
placeholders[key] = match.group()
text = text.replace(match.group(), key, 1)
counter += 1
# Protect citations
for match in re.finditer(r'\\cite\{[^}]+\}|\([A-Z][a-z]+(?:\s+et\s+al\.)?,\s*\d{4}\)', text):
key = f'__CITE_{counter}__'
placeholders[key] = match.group()
text = text.replace(match.group(), key, 1)
counter += 1
# Protect URLs
for match in re.finditer(r'https?://\S+', text):
key = f'__URL_{counter}__'
placeholders[key] = match.group()
text = text.replace(match.group(), key, 1)
counter += 1
return text, placeholders
def restore_elements(text: str, placeholders: dict) -> str:
"""Restore protected elements from placeholders."""
for key, value in placeholders.items():
text = text.replace(key, value)
return text
For critical documents, perform back-translation on a random 10-20% sample of paragraphs. Compare the back-translated text with the original to identify semantic drift. Flag any paragraph where back-translation diverges significantly from the source.