1. Machine Translation Challenges 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 211-731: Machine Translation

3. January 12, 2011 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 snub doesn’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) 311-731: Machine Translation

4. January 12, 2011 Google at Work… 411-731: Machine Translation

5. January 12, 2011511-731: Machine Translation

6. January 12, 2011611-731: Machine Translation

7. January 12, 2011 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”) 711-731: Machine Translation

8. January 12, 2011 [From Hutchins & Somers] Lexical Differences 811-731: Machine Translation

9. January 12, 2011 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 911-731: Machine Translation

10. January 12, 2011 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) 1011-731: Machine Translation

11. January 12, 20111111-731: Machine Translation

12. January 12, 20111211-731: Machine Translation

13. January 12, 20111311-731: Machine Translation

14. January 12, 2011 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 1411-731: Machine Translation

15. January 12, 2011 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 1511-731: Machine Translation

16. January 12, 2011 Syntax-to-Semantics Differences Structure-change example: I like swimming “Ich scwhimme gern” I swim gladly 1611-731: Machine Translation

17. January 12, 20111711-731: Machine Translation

18. January 12, 2011 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 1811-731: Machine Translation

19. January 12, 20111911-731: Machine Translation

20. January 12, 20112011-731: Machine Translation

21. January 12, 2011 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 2111-731: Machine Translation

22. January 12, 20112211-731: Machine Translation

23. January 12, 20112311-731: Machine Translation

24. January 12, 2011 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. 2411-731: Machine Translation

25. January 12, 2011 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 2511-731: Machine Translation

26. January 12, 2011 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? 2611-731: Machine Translation

27. January 12, 20112711-731: Machine Translation

28. January 12, 2011 Formulaic Utterances Good night. tisbaH cala xEr waking up on good Romanization of Arabic from CallHome Egypt 2811-731: Machine Translation

29. January 12, 2011 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. 2911-731: Machine Translation

30. January 12, 20113011-731: Machine Translation

31. January 12, 2011 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 3111-731: Machine Translation

32. January 12, 2011 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? 3211-731: Machine Translation

33. January 12, 2011 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 3311-731: Machine Translation

34. January 12, 2011 Homework Assignment #1 3411-731: Machine Translation

35. January 12, 2011 Questions… 3511-731: Machine Translation