Multilingual Research GuideSAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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"
---

# Academic Translation Guide

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.

## Translation Workflow

### End-to-End Pipeline

```
Source Document
  |
  v
1. Document Preparation
   - Extract text (OCR if scanned)
   - Identify formulas, figures, tables (do NOT translate these)
   - Build terminology glossary
  |
  v
2. Segmentation
   - Split into translatable units (sentences/paragraphs)
   - Tag non-translatable elements: equations, citations, proper nouns
  |
  v
3. Translation
   - Apply machine translation (first pass)
   - Human post-editing (second pass)
   - Terminology consistency check (third pass)
  |
  v
4. Quality Assurance
   - Back-translation verification (sample)
   - Domain expert review
   - Formatting and citation check
  |
  v
Target Document
```

## Terminology Management

### Building a Domain Glossary

```python
import json

def build_terminology_glossary(source_text: str, domain: str,
                                source_lang: str = 'zh',
                                target_lang: str = 'en') -> list[dict]:
    """
    Extract and standardize technical terms from source text.

    Args:
        source_text: Raw text of the source document
        domain: Research domain (e.g., 'machine_learning', 'biochemistry')
        source_lang: Source language code
        target_lang: Target language code
    Returns:
        List of terminology entries
    """
    # Common domain-specific glossaries
    glossaries = {
        'machine_learning': {
            'zh_en': {
                '过拟合': 'overfitting',
                '欠拟合': 'underfitting',
                '梯度下降': 'gradient descent',
                '损失函数': 'loss function',
                '卷积神经网络': 'convolutional neural network',
                '注意力机制': 'attention mechanism',
                '预训练模型': 'pre-trained model',
                '微调': 'fine-tuning',
                '批归一化': 'batch normalization',
                '学习率': 'learning rate'
            }
        },
        'biochemistry': {
            'zh_en': {
                '蛋白质折叠': 'protein folding',
                '酶动力学': 'enzyme kinetics',
                '基因表达': 'gene expression',
                '转录因子': 'transcription factor',
                '信号通路': 'signaling pathway',
                '代谢组学': 'metabolomics'
            }
        }
    }

    domain_terms = glossaries.get(domain, {}).get(f'{source_lang}_{target_lang}', {})

    entries = []
    for source_term, target_term in domain_terms.items():
        if source_term in source_text:
            entries.append({
                'source': source_term,
                'target': target_term,
                'domain': domain,
                'verified': True,
                'notes': ''
            })
    return entries
```

### Terminology Consistency Enforcement

```python
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
```

## Machine Translation Integration

### Using DeepL API for Academic Text

```python
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
```

### Protecting Non-Translatable Elements

Before sending text to any translation engine, protect elements that should not be translated:

```python
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.gro

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

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No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.