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 ->
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 ->
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