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Machine Translation Challenges and Language Divergences

Machine Translation Challenges and Language Divergences. Alon Lavie Language Technologies Institute Carnegie Mellon University 11-731: Machine Translation January 12, 2011. Major Sources of Translation Problems. Lexical Differences:

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Machine Translation Challenges and Language Divergences

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  1. Machine TranslationChallenges and Language Divergences Alon Lavie Language Technologies Institute Carnegie Mellon University 11-731: Machine Translation January 12, 2011

  2. Major Sources of Translation Problems • Lexical Differences: • Multiple possible translations for SL word, or difficulties expressing SL word meaning in a single TL word • Structural Differences: • Syntax of SL is different than syntax of the TL: word order, sentence and constituent structure • Differences in Mappings of Syntax to Semantics: • Meaning in TL is conveyed using a different syntactic structure than in the SL • Idioms and Constructions 11-731: Machine Translation

  3. Lexical Differences • SL word has several different meanings, that translate differently into TL • Ex: financial bank vs. river bank • Lexical Gaps: SL word reflects a unique meaning that cannot be expressed by a single word in TL • Ex: English snubdoesn’t have a corresponding verb in French or German • TL has finer distinctions than SL  SL word should be translated differently in different contexts • Ex: English wall can be German wand(internal), mauer (external) 11-731: Machine Translation

  4. Google at Work… 11-731: Machine Translation

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  7. Lexical Differences • Lexical gaps: • Examples: these have no direct equivalent in English:gratiner(v., French, “to cook with a cheese coating”)ōtosanrin(n., Japanese, “three-wheeled truck or van”) 11-731: Machine Translation

  8. Lexical Differences [From Hutchins & Somers] 11-731: Machine Translation

  9. MT Handling of Lexical Differences • Direct MT and Syntactic Transfer: • Lexical Transfer stage uses bilingual lexicon • SL word can have multiple translation entries, possibly augmented with disambiguation features or probabilities • Lexical Transfer can involve use of limited context (on SL side, TL side, or both) • Lexical Gaps can partly be addressed via phrasal lexicons • Semantic Transfer: • Ambiguity of SL word must be resolved during analysis  correct symbolic representation at semantic level • TL Generation must select appropriate word or structure for correctly conveying the concept in TL 11-731: Machine Translation

  10. Structural Differences • Syntax of SL is different than syntax of the TL: • Word order within constituents: • English NPs: art adj n the big boy • Hebrew NPs: art n art adj ha yeled ha gadol • Constituent structure: • English is SVO: Subj Verb Obj I saw the man • Modern Arabic is VSO: Verb Subj Obj • Different verb syntax: • Verb complexes in English vs. in German I can eat the apple Ich kann den apfel essen • Case marking and free constituent order • German and other languages that mark case: den apfel esse Ich the(acc) apple eat I(nom) 11-731: Machine Translation

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  14. MT Handling of Structural Differences • Direct MT Approaches: • No explicit treatment: Phrasal Lexicons and sentence level matches or templates • Syntactic Transfer: • Structural Transfer Grammars • Trigger rule by matching against syntactic structure on SL side • Rule specifies how to reorder and re-structure the syntactic constituents to reflect syntax of TL side • Semantic Transfer: • SL Semantic Representation abstracts away from SL syntax to functional roles  done during analysis • TL Generation maps semantic structures to correct TL syntax 11-731: Machine Translation

  15. Syntax-to-Semantics Differences • Meaning in TL is conveyed using a different syntactic structure than in the SL • Changes in verb and its arguments • Passive constructions • Motion verbs and state verbs • Case creation and case absorption • Main Distinction from Structural Differences: • Structural differences are mostly independent of lexical choices and their semantic meaning  addressed by transfer rules that are syntactic in nature • Syntax-to-semantic mapping differences are meaning-specific: require the presence of specific words (and meanings) in the SL 11-731: Machine Translation

  16. Syntax-to-Semantics Differences • Structure-change example: I like swimming “Ich scwhimme gern” I swim gladly 11-731: Machine Translation

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  18. Syntax-to-Semantics Differences • Verb-argument example: Jones likes the film. “Le film plait à Jones.” (lit: “the film pleases to Jones”) • Use of case roles can eliminate the need for this type of transfer • Jones = Experiencer • film = Theme 11-731: Machine Translation

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  21. Syntax-to-Semantics Differences • Passive Constructions • Example: French reflexive passives:Ces livres se lisent facilement*”These books read themselves easily”These books are easily read 11-731: Machine Translation

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  24. Same intention, different syntax • rigly bitiwgacny my leg hurts • candy wagac fE rigly I have pain in my leg • rigly bitiClimny my leg hurts • fE wagac fE rigly there is pain in my leg • rigly bitinqaH calya my leg bothers on me Romanization of Arabic from CallHome Egypt. 11-731: Machine Translation

  25. MT Handling of Syntax-to-Semantics Differences • Direct MT Approaches: • No Explicit treatment: Phrasal Lexicons and sentence level matches or templates • Syntactic Transfer: • “Lexicalized” Structural Transfer Grammars • Trigger rule by matching against “lexicalized” syntactic structure on SL side: lexical and functional features • Rule specifies how to reorder and re-structure the syntactic constituents to reflect syntax of TL side • Semantic Transfer: • SL Semantic Representation abstracts away from SL syntax to functional roles  done during analysis • TL Generation maps semantic structures to correct TL syntax 11-731: Machine Translation

  26. Idioms and Constructions • Main Distinction: meaning of whole is not directly compositional from meaning of its sub-parts  no compositional translation • Examples: • George is a bull in a china shop • He kicked the bucket • Can you please open the window? 11-731: Machine Translation

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  28. Formulaic Utterances • Good night. • tisbaH cala xEr waking up on good • Romanization of Arabic from CallHome Egypt 11-731: Machine Translation

  29. Constructions • Identifying speaker intention rather than literal meaning for formulaic and task-oriented sentences. How about … suggestion Why don’t you… suggestion Could you tell me… request info. I was wondering… request info. 11-731: Machine Translation

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  31. MT Handling of Constructions and Idioms • Direct MT Approaches: • No Explicit treatment: Phrasal Lexicons and sentence level matches or templates • Syntactic Transfer: • No effective treatment • “Highly Lexicalized” Structural Transfer rules can handle some constructions • Trigger rule by matching against entire construction, including structure on SL side • Rule specifies how to generate the correct construction on the TL side • Semantic Transfer: • Analysis must capture non-compositional representation of the idiom or construction  specialized rules • TL Generation maps construction semantic structures to correct TL syntax and lexical words 11-731: Machine Translation

  32. Take Home Messages • Remember these types of language divergences as you learn about and apply the various steps in the MT system pipelines of different approaches! • Ask yourself how capable these various steps and approaches are in addressing these types of divergences! • Can the step/approach handle these divergences? • If so, does it model the divergence at the appropriate level of abstraction? • Keep these language divergences in mind when you analyze the errors of the MT system that you have put together and trained! • Are the errors attributable to a particular divergence? • What would be required for the system to address this type of error? 11-731: Machine Translation

  33. Summary • Main challenges for current state-of-the-art MT approaches - Coverage and Accuracy: • Acquiring broad-coverage high-accuracy translation lexicons (for words and phrases) • learning syntactic mappings between languages from parallel word-aligned data • overcoming syntax-to-semantics differences and dealing with constructions • Effective Target Language Modeling 11-731: Machine Translation

  34. Homework Assignment #1 11-731: Machine Translation

  35. Questions… 11-731: Machine Translation

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