1. Towards Syntactically Constrained Statistical Word Alignment Greg Hanneman 11-734: Advanced Machine Translation Seminar April 30, 2008

2. Outline The word alignment problem Base approaches Syntax-based approaches –Distortion models –Tree-to-string models –Tree-to-tree models Discussion

3. Word Alignment Parallel sentence pair: F and E Most general: map a subset of F to a subset of E

4. Word Alignment Very large alignment spaces! –An n-word parallel sentence has n 2 possible links and 2 n 2 possible alignments –Restrict to one-to-one alignments: n! possible alignments Alignment models try to restrict or learn a probability distribution over this space to get the “best” alignment of a sentence

5. Outline The word alignment problem Base approaches Syntax-based approaches –Distortion models –Tree-to-string models –Tree-to-tree models Discussion

6. A Generative Story [Brown et al. 1990] Theproposalwillnotbeimplemented English sentence Fertility Lespropositionsneserontpasapplicationmisesen Lexical generation Lespropositionsneserontpasapplicationmisesen Distortion

7. The Framework F: words f 1 … f j … f n E: words e 1 … e i … e m Compute P(F, A | E) for hidden alignment variable A: a 1 … a j … a n –The major step: decomposition, model parameters, EM algorithm, etc. a j = i: word f j is aligned to word e i

8. The IBM Models [Brown et al. 1993; Och and Ney 2003] Model 1: “Bag of words” — word order doesn’t affect alignment Model 2: Position of words being aligned does matter

9. The IBM Models [Brown et al. 1993; Och and Ney 2003] Later models use more implicit structural or linguistic information, but not really syntax, and not really overtly –Fertility: P(φ | e i ) of e i producing φ words in F –Distortion: P(τ, π | E) for a set of F words τ in a permutation π –Previous alignments: Probs. for positions in F of the different words of a fertile e i

10. The HMM Model [Vogel et al. 1996; Och and Ney 2003] Linguistic intuition: words, and their alignments, tend to clump together in clusters a j depends on absolute size of “jump” between it and a j–1

11. Discriminative Training Consider all possible alignments, score them, and pick the best ones under some set of constraints Can incorporate arbitrary features; generative models more fixed Generative models’ EM requires lots of unlabeled training data; discriminative requires some labeled data

12. Discriminative Alignment [Taskar et al. 2005] –Co-occurrence –Position difference –Co-occurrence of following words –Word-frequency rank –Model 4 prediction –…–… The proposal will not be implemented Les propositions ne seront pas application mises en

13. Outline The word alignment problem Base approaches Syntax-based approaches –Distortion models –Tree-to-string models –Tree-to-tree models Discussion

14. Syntax-Based Approaches Constrain alignment space by looking beyond flat text stream: take higher-level sentence structure into account Representations –Constituency structure –Inversion Transduction Grammar –Dependency structure

15. An MT Motivation

16. Syntax-Based Distortion [DeNero and Klein 2007] Syntax-based MT should start from syntax-aware word alignments HMM model + target-language parse trees: prefer alignments that respect tree Handled in distortion model: jumps should reflect tree structure

17. Syntax-Based Distortion [DeNero and Klein 2007] HMM distortion: size of jump between a j–1 and a j Syntactic distortion: tree path between a j–1 and a j

18. Syntax-Based Distortion [DeNero and Klein 2007] Training:100,000 parallel French–English and Chinese–English sentences with English parse trees Both E → F and F → E; combined with different unions and intersections, plus thresholds Test: Hand-aligned Hansards and NIST MT 2002 data

19. Syntax-Based Distortion [DeNero and Klein 2007] HMMs roughly equal, better than GIZA++ Soft union for French; hard union for Chinese; competitive thresholding

20. Tree-to-String Models

21. New generative story Word-level fertility and distortion replaced with node insertion and sibling reordering Lexical translation still the same Word alignment produced as a side effect from lexical translations

22. Tree-to-String Alignment [Yamada and Knight 2001] Discussed in other sessions this semester Training: 2121 short Japanese–English sentences, modified Collins parser output for English Test: First 50 sentences of training corpus Beat IBM Model 5 on human judgements; perplexity between Model 1 and Model 5

23. Subtree Cloning [Gildea 2003] Original tree-to-string model is too strict –Syntactic divergences, reordering Soft constraint: allow alignments that violate tree structure, but at a cost –Tweak the tree side of the alignment to contain things needed for the string side –Ex.: SVO to OSV

24. Subtree Cloning [Gildea 2003] S VP AUXVP doADVPVB RB entirely understand NP I PRP NP PRP$NN yourlanguage NP I PRP

25. Subtree Cloning [Gildea 2003] S VP AUXVP do NP I PRP ADVPVB RB entirely understand NP PRP$NN yourlanguage NP I PRP

26. Subtree Cloning [Gildea 2003] S VP AUXVP do NP I PRP ADVPVB RB entirely understand NP PRP$NN yourlanguage NP I PRP menti NULL nihuawotu tung

27. Subtree Cloning [Gildea 2003] For a node n p : –Probability of cloning something as a new child of n p : single EM-learned constant for all n p –Probability of making that clone a node n c : uniform over all n c Surprising that this works…

28. Subtree Cloning [Gildea 2003] Compared with IBM 1–3, basic tree-to- string, basic tree-to-tree models Training: 4982 Korean–English sentence pairs, with manual Korean parse trees Test: 101 hand-aligned held-out sentences

29. Subtree Cloning [Gildea 2003] Cloning helps: as good or better than IBM Tree-to-tree model runs faster

30. Tree-to-Tree Models Alignment must conform to tree structure on both sides — space is more constrained Requires more transformation operations to handle divergent structures [Gildea 2003] Or we could be more permissive…

31. Inversion Transduction Grammar [Wu 1997] For bilingual parsing; get one- to-one word alignment as a side effect Parallel binary-branching trees with reordering

32. ITG Operations A → [A A] –Produce “A 1 A 2 ” in source and target streams A → –Produce “A 1 A 2 ” in source stream, “A 2 A 1 ” in target stream A → e / f –Produce “e” in source stream, “f” in target stream

33. ITG Operations “Canonical form” ITG produces only one derivation for a given alignment –S → A | B | C –A → [A B] | [B B] | [C B] | [A C] | [B C] | [C C] –B → | | | | | –C → e / f

34. Alignment with ITG [Zhang and Gildea 2004] Compared IBM 1, IBM 4, ITG, and tree-to- string (with and without cloning) Training: Chinese–English (18,773) and French–English (20,000) sentences less than 25 words long Test: Hand-aligned Chinese–English (48) and French–English (447)

35. Alignment with ITG [Zhang and Gildea 2004] ITG best, or at least as good as IBM or tree-to-string plus cloning ITG has no linguistic syntax…

36. Dependency Parsing Discussed in other sessions this semester Notion of violating “phrasal cohesion” –Usually bad, but not always

37. Dependencies + ITG [Cherry and Lin 2006] Find invalid dependency spans; assign score of –∞ if used by the ITG parser Simple model: maximize co-occurrence score with penalty for distant words ITG reduces AER by 13% relative; dependencies + ITG reduce by 34%

38. Dependencies + ITG [Cherry and Lin 2006] Discriminative training with an SVM Feature vector for each ITG rule instance –Features from Taskar et al. [2005] –Feature marking ITG inversion rules –Feature (penalty) marking invalid spans based on dependency tree

39. Dependencies + ITG [Cherry and Lin 2006] Compared Taskar et al. to D-ITG with hard and soft constraints Training: 50,000 French–English sentence pairs for counts and probabilities; 100 hand-annotated pairs with derived ITG trees for discriminative training Test: 347 hand-annotated sentences from 2003 parallel text workshop

40. Dependencies + ITG [Cherry and Lin 2006] Relative improvement smaller in discriminative training scenario with stronger objective function Hard constraint starts to hurt recall

41. Outline The word alignment problem Base approaches Syntax-based approaches –Distortion models –Tree-to-string models –Tree-to-tree models Discussion

42. All These Tradeoffs… Mathematical and statistical correctness vs. computability Simple model vs. capturing linguistic phenomena Not enough syntactic information vs. too much syntactic information Ruling out bad alignments vs. keeping good alignments around

43. Completely unconstrained: every alignment link (e i, f j ) either “on” or “off” Permutation space: one-to-one alignment with reordering [Taskar et al. 2005] ITG space: permutation space satisfying binary tree constraint [Wu 1997] Dependency space: permutation space maintaining phrasal cohesion Alignment Spaces

44. D-ITG space: Dependency ∩ ITG space [Cherry and Lin 2006] HD-ITG space: D-ITG space where each span must contain a head [Cherry and Lin 2006a]

45. Examining Alignment Spaces [Cherry and Lin 2006a] Alignment score –Learned co-occurrence score –Gold-standard oracle score

46. Examining Alignment Spaces [Cherry and Lin 2006a] Learned co-occurrence score –More restricted spaces give better results

47. Examining Alignment Spaces [Cherry and Lin 2006a] Oracle score: subsets of permutation space –ITG rules out almost nothing correct –Beam search in dependency space does worst

48. Conclusions Base alignment models are mathematical, limited notions of sentence structure Syntax-aware alignment helpful for syntax-aware MT [DeNero and Klein 2007] Using structure as a hard constraint is harmful for divergent sentences; tweaking trees [Gildea 2003] or using soft constraints [Cherry and Lin 2006] helps fix this

49. Conclusions Surprise winner: ITG –Computationally straightforward –Permissive, simple grammar that mostly only rules out bad alignments [Cherry and Lin 2006a] –Does a lot, even when it’s not the best Discriminative framework looks promising and flexible — can incorporate generative models as features [Taskar et al. 2005]

50. Towards the Future Easy-to-run GIZA++ made complicated IBM models the norm — promising discriminative or syntax-based models currently lack such a toolkit Syntax-based discriminative techniques — morphology, POS, semantic information… Any other ideas?

51. References Brown, P., J. Cocke, S. Della Pietra, V. Della Pietra, F. Jelinek, J. Lafferty, R. Mercer, and P. Roossin, “A statistical approach to machine translation,” Computational Linguistics, 16(2):79-85, 1990. Brown, P., S. Della Pietra, V. Della Pietra, and R. Mercer, “The mathematics of statistical machine translation: Parameter estimation,” Computational Linguistics, 19(2):263-311. Cherry, Colin and Dekang Lin, “Soft syntactic constraints for word alignment through discriminative training,” Proceedings of the COLING/ACL Poster Session, 105-112, 2006. Cherry, Colin and Dekang Lin, “A comparison of syntactically motivated alignment spaces,” Proceedings of EACL, 145-152, 2006a. DeNero, John and Dan Klein, “Tailoring word alignments to syntactic machine translation,” Proceedings of ACL, 17-24, 2007. Gildea, Daniel, “Loosely tree-based alignment for machine translation,” Proceedings of ACL, 80-87, 2003.

52. References Och, Franz and Hermann Ney, “A systematic comparison of various statistical alignment models,” Computational Linguistics, 29(1):19-51, 2003. Taskar, B., S. Lacoste-Julien, and D. Klein, “A discriminative matching approach to word alignment,” Proceedings of HLT/EMNLP, 73-80, 2005. Vogel, S., H. Ney, and C. Tillmann, “HMM-based word alignment in statistical translation,” Proceedings of COLING, 836-841, 1996. Wu, Dekai, “Stochastic inversion transduction grammars and bilingual parsing of parallel corpora,” Computational Linguistics, 23(3):377-403. Yamada, Kenji and Kevin Knight, “A syntax-based statistical translation model,” Proceedings of ACL, 523-530, 2001. Zhang, Hao and Daniel Gildea, “Syntax-based alignment: Supervised or unsupervised?” Proceedings of COLING, 418-424, 2004.