Translation Assistant

The Translation Assistant skill guides you through implementing multilingual translation systems that bridge language barriers accurately and culturally appropriately. From simple phrase translation to full document localization, this skill covers the spectrum of translation needs.

Modern translation has been transformed by neural machine translation and large language models, but effective translation still requires understanding context, domain, and cultural nuances. This skill helps you choose the right tools, handle translation quality, and build systems that work across languages.

Whether you're translating user interfaces, customer communications, technical documentation, or creative content, this skill ensures your translations are accurate, natural, and culturally appropriate.

Core Workflows

Workflow 1: Choose Translation Approach

• Assess requirements:

• Language pairs needed

• Domain specificity

• Quality requirements

• Volume and speed needs

• Budget constraints

• Compare options:

Approach Quality Speed Cost Best For

Google Translate API Good Fast $ General, high volume

DeepL Very good Fast $$ European languages, quality

OpenAI/Anthropic Excellent Medium $$$ Nuanced, context-heavy

Custom NMT Domain-specific Fast Setup cost Specialized domains

Human + MT Best Slow $$$$ Critical content

• Select based on tradeoffs

• Plan quality assurance process

Workflow 2: Implement Translation Pipeline

• Set up translation service: from google.cloud import translate_v2 as translate

class TranslationPipeline: def init(self, provider="google"): if provider == "google": self.client = translate.Client() elif provider == "deepl": self.client = deepl.Translator(auth_key) elif provider == "llm": self.client = LLMTranslator()

def translate(self, text, source_lang, target_lang):
    # Preprocess
    prepared = self.preprocess(text, source_lang)

    # Translate
    if self.provider == "google":
        result = self.client.translate(
            prepared,
            source_language=source_lang,
            target_language=target_lang
        )
        translated = result["translatedText"]
    elif self.provider == "llm":
        translated = self.llm_translate(prepared, source_lang, target_lang)

    # Postprocess
    final = self.postprocess(translated, target_lang)

    return final

• Handle special content:

• Preserve placeholders and variables

• Handle HTML/markup

• Maintain formatting

• Validate translation quality

• Add caching for repeated content

Workflow 3: Build Localization System

• Extract translatable content: def extract_strings(source_files): """Extract strings needing translation.""" strings = [] for file in source_files: # Find translatable strings content = read_file(file) matches = find_translatable(content)

    for match in matches:
        strings.append({
            "key": generate_key(match),
            "source": match.text,
            "context": match.surrounding_context,
            "file": file,
            "line": match.line
        })
  

  return strings

• Translate with context: def translate_with_context(strings, target_lang): results = [] for s in strings: translation = translate( text=s["source"], context=s["context"], target_lang=target_lang ) results.append({ **s, "translation": translation, "target_lang": target_lang }) return results

• Store in translation management:

• Translation memory for consistency

• Glossary for terminology

• Version control for changes

• Deploy localized content

Quick Reference

Action Command/Trigger

Translate text "Translate [text] to [language]"

Choose service "Best translation for [use case]"

Handle domain terms "Translation glossary for [domain]"

Quality check "Check translation quality"

Localize app "Localize UI for [languages]"

Batch translate "Translate [N] documents"

Best Practices

Provide Context: Translation quality depends on context

• Include surrounding text

• Specify domain/subject matter

• Note tone and register (formal/informal)

Maintain Terminology Consistency: Key terms should translate consistently

• Build domain glossaries

• Use translation memory

• Review terminology with stakeholders

Preserve Formatting and Variables: Technical content has special needs

• Protect placeholders ({name}, %s, etc.)

• Maintain HTML/markdown structure

• Handle number and date formats

Handle Untranslatable Content: Some things shouldn't be translated

• Brand names and trademarks

• Technical identifiers and codes

• Legal disclaimers (sometimes)

Quality Assurance is Essential: Machine translation makes mistakes

• Back-translation for verification

• Native speaker review

• Automated quality checks

Consider Cultural Adaptation: Translation != localization

• Date and number formats

• Currency and units

• Cultural references and idioms

• Right-to-left languages

Advanced Techniques

LLM-Based Contextual Translation

Use language models for nuanced translation:

def llm_translate(text, source_lang, target_lang, context=None, style=None): prompt = f"""Translate the following text from {source_lang} to {target_lang}.

{"Context: " + context if context else ""} {"Style: " + style if style else ""}

Important guidelines:

• Maintain the meaning and tone of the original
• Use natural, fluent {target_lang}
• Preserve any formatting, placeholders, or special characters
• If there are cultural references, adapt them appropriately

Source text: {text}

Translation:"""

return llm.complete(prompt)

Example with context

result = llm_translate( text="The app crashed when I clicked submit.", source_lang="English", target_lang="Japanese", context="This is a bug report from a user", style="Formal technical support" )

Translation Memory System

Reuse previous translations for consistency:

class TranslationMemory: def init(self): self.memory = {} # source -> {lang: translation} self.fuzzy_index = FuzzyMatcher()

def add(self, source, target_lang, translation):
    if source not in self.memory:
        self.memory[source] = {}
    self.memory[source][target_lang] = translation
    self.fuzzy_index.add(source)

def lookup(self, source, target_lang, fuzzy_threshold=0.8):
    # Exact match
    if source in self.memory and target_lang in self.memory[source]:
        return {
            "match_type": "exact",
            "translation": self.memory[source][target_lang],
            "confidence": 1.0
        }

    # Fuzzy match
    matches = self.fuzzy_index.search(source, threshold=fuzzy_threshold)
    if matches:
        best = matches[0]
        if target_lang in self.memory[best.text]:
            return {
                "match_type": "fuzzy",
                "original_source": best.text,
                "translation": self.memory[best.text][target_lang],
                "confidence": best.score
            }

    return None

def translate_with_memory(self, text, target_lang):
    # Check memory first
    cached = self.lookup(text, target_lang)
    if cached and cached["confidence"] > 0.95:
        return cached["translation"]

    # Translate fresh
    translation = translate_api(text, target_lang)

    # Store in memory
    self.add(text, target_lang, translation)

    return translation

Domain Glossary Management

Ensure consistent terminology:

class TranslationGlossary: def init(self, domain): self.domain = domain self.terms = {} # source_term -> {lang: translated_term}

def add_term(self, source, translations):
    self.terms[source.lower()] = translations

def apply_to_translation(self, source_text, target_lang, translation):
    """
    Ensure glossary terms are used correctly in translation.
    """
    corrections = []
    source_lower = source_text.lower()

    for term, translations in self.terms.items():
        if term in source_lower and target_lang in translations:
            expected = translations[target_lang]
            if expected.lower() not in translation.lower():
                corrections.append({
                    "source_term": term,
                    "expected": expected,
                    "found": False
                })

    if corrections:
        # Re-translate with glossary enforcement
        return self.translate_with_glossary(source_text, target_lang)

    return translation

def translate_with_glossary(self, text, target_lang):
    glossary_context = "\n".join([
        f"'{term}' should be translated as '{trans[target_lang]}'"
        for term, trans in self.terms.items()
        if target_lang in trans
    ])

    prompt = f"""Translate to {target_lang}, using these required terms:

{glossary_context}

Text: {text}"""

    return llm.complete(prompt)

Quality Estimation

Automatically assess translation quality:

def estimate_translation_quality(source, translation, source_lang, target_lang): """ Estimate translation quality without reference translation. """ checks = []

# Check 1: Back-translation similarity
back_translated = translate(translation, target_lang, source_lang)
back_similarity = compute_similarity(source, back_translated)
checks.append({
    "check": "back_translation",
    "score": back_similarity,
    "details": {"back_translated": back_translated}
})

# Check 2: Length ratio (translations should be similar length)
length_ratio = len(translation) / max(len(source), 1)
expected_ratio = get_expected_length_ratio(source_lang, target_lang)
length_score = 1 - abs(length_ratio - expected_ratio) / expected_ratio
checks.append({
    "check": "length_ratio",
    "score": max(0, length_score),
    "details": {"ratio": length_ratio, "expected": expected_ratio}
})

# Check 3: LLM quality assessment
quality_prompt = f"""Rate this translation from 1-10 for accuracy and fluency.

Source ({source_lang}): {source} Translation ({target_lang}): {translation}

Provide scores and brief explanation."""

llm_assessment = llm.complete(quality_prompt)
checks.append({
    "check": "llm_assessment",
    "score": parse_score(llm_assessment) / 10,
    "details": {"assessment": llm_assessment}
})

# Combined score
overall = sum(c["score"] for c in checks) / len(checks)

return {
    "overall_score": overall,
    "checks": checks,
    "recommendation": "accept" if overall > 0.8 else "review"
}

Batch Translation with Consistency

Translate large volumes while maintaining consistency:

async def batch_translate_consistent(texts, target_lang, batch_size=50): """ Translate many texts while maintaining terminology consistency. """ # Step 1: Extract unique terms for glossary all_text = " ".join(texts) key_terms = extract_key_terms(all_text)

# Step 2: Translate key terms first for consistency
term_translations = {}
for term in key_terms:
    translation = await translate_with_verification(term, target_lang)
    term_translations[term] = translation

# Step 3: Batch translate with glossary context
results = []
for batch in chunk(texts, batch_size):
    batch_results = await asyncio.gather(*[
        translate_with_glossary(text, target_lang, term_translations)
        for text in batch
    ])
    results.extend(batch_results)

return results

Common Pitfalls to Avoid

• Translating without context, leading to wrong word choices

• Inconsistent terminology across a project

• Not handling placeholders and variables correctly

• Ignoring cultural differences (dates, currencies, idioms)

• Trusting machine translation without quality checks

• Not maintaining translation memory for consistency

• Forgetting about text expansion (translations are often longer)

• Ignoring right-to-left language considerations