Multilingual Research Guide

Source Document
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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 = '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

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

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