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Forest-based Algorithms in Natural Language Processing Liang Huang - PowerPoint PPT Presentation

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Forest-based Algorithms in Natural Language Processing Liang Huang overview of Ph.D. work done at Penn (and ISI, ICT) INSTITUTE OF COMPUTING TECHNOLOGY includes joint work with David Chiang, Kevin Knight, Aravind Joshi, Haitao Mi and Qun Liu

  1. Redundancies in n-best lists Not all those who wrote oppose the changes. (TOP (S (NP (NP (RB Not) (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (DT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (DT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VB oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (RB all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VB oppose) (NP (DT the) (NNS changes))) (. .))) ... Forest Algorithms 26

  2. Redundancies in n-best lists Not all those who wrote oppose the changes. (TOP (S (NP (NP (RB Not) (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (DT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (DT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VB oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (NP (NP (RB Not) (RB all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VBP oppose) (NP (DT the) (NNS changes))) (. .))) (TOP (S (RB Not) (NP (NP (PDT all) (DT those)) (SBAR (WHNP (WP who)) (S (VP (VBD wrote))))) (VP (VB oppose) (NP (DT the) (NNS changes))) (. .))) packed forest ... Forest Algorithms 26

  3. Reranking on a Forest? • with only local features (Solution 2) • dynamic programming, exact, tractable (Taskar et al. 2004; McDonald et al., 2005) • with non-local features (Solution 3) • on-the-fly reranking at internal nodes • top k derivations at each node • use as many non-local features as possible at each node • chart parsing + discriminative reranking • we use perceptron for simplicity Forest Algorithms 27

  4. Features • a feature f is a function from tree y to a real number • f 1 ( y )=log Pr( y ) is the log Prob from generative parser • every other feature counts the number of times a particular configuration occurs in y our features are from TOP (Charniak & Johnson, 2005) S (Collins, 2000) NP VP . instances of Rule feature PRP VBD NP PP . f 100 ( y ) = f S → NP VP . ( y ) = 1 I saw DT NN IN NP f 200 ( y ) = f NP → DT NN ( y ) = 2 the boy with DT NN a telescope Forest Algorithms 28

  5. Local vs. Non-Local Features • a feature is local iff. it can be factored among local productions of a tree (i.e., hyperedges in a forest) • local features can be pre-computed on each hyperedge in the forest; non-locals can not TOP ParentRule is non-local S NP VP . PRP VBD NP PP . Rule is local I saw DT NN IN NP the boy with DT NN a telescope Forest Algorithms 29

  6. Local vs. Non-Local: Examples • CoLenPar feature captures the difference in lengths of adjacent conjuncts (Charniak and Johnson, 2005) local! CoLenPar: 2 Forest Algorithms 30

  7. Local vs. Non-Local: Examples • CoPar feature captures the depth to which adjacent conjuncts are isomorphic (Charniak and Johnson, 2005) non-local! (violates DP principle) CoPar: 4 Forest Algorithms 31

  8. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at VP node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 32

  9. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at VP node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 32

  10. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at VP node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 32

  11. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at S node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 33

  12. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at S node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 33

  13. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at S node NP VP . local features factor PRP VBD NP PP . across hyperedges statically I saw DT NN IN NP non-local features factor the boy with DT NN across nodes dynamically a telescope Forest Algorithms 33

  14. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at TOP node NP VP . non-local features factor PRP VBD NP PP . across nodes dynamically I saw DT NN IN NP local features factor the boy with DT NN across hyperedges statically a telescope Forest Algorithms 34

  15. Factorizing non-local features • going bottom-up, at each node • compute (partial values of) feature instances that become computable at this level • postpone those uncomputable to ancestors TOP unit instance of ParentRule S feature at TOP node NP VP . non-local features factor PRP VBD NP PP . across nodes dynamically I saw DT NN IN NP local features factor the boy with DT NN across hyperedges statically a telescope Forest Algorithms 34

  16. NGramTree (C&J 05) • an NGramTree captures the smallest tree fragment that contains a bigram (two consecutive words) • unit instances are boundary words between subtrees TOP A i,k S B i,j C j,k NP VP . PRP VBD NP PP . w i . . . w j − 1 w j . . . w k − 1 I saw DT NN IN NP unit instance of node A the boy with DT NN a telescope Forest Algorithms 35

  17. NGramTree (C&J 05) • an NGramTree captures the smallest tree fragment that contains a bigram (two consecutive words) • unit instances are boundary words between subtrees TOP A i,k S B i,j C j,k NP VP . PRP VBD NP PP . w i . . . w j − 1 w j . . . w k − 1 I saw DT NN IN NP unit instance of node A the boy with DT NN a telescope Forest Algorithms 36

  18. NGramTree (C&J 05) • an NGramTree captures the smallest tree fragment that contains a bigram (two consecutive words) • unit instances are boundary words between subtrees TOP A i,k S B i,j C j,k NP VP . PRP VBD NP PP . w i . . . w j − 1 w j . . . w k − 1 I saw DT NN IN NP unit instance of node A the boy with DT NN a telescope Forest Algorithms 36

  19. NGramTree (C&J 05) • an NGramTree captures the smallest tree fragment that contains a bigram (two consecutive words) • unit instances are boundary words between subtrees TOP A i,k S B i,j C j,k NP VP . PRP VBD NP PP . w i . . . w j − 1 w j . . . w k − 1 I saw DT NN IN NP unit instance of node A the boy with DT NN a telescope Forest Algorithms 37

  20. NGramTree (C&J 05) • an NGramTree captures the smallest tree fragment that contains a bigram (two consecutive words) • unit instances are boundary words between subtrees TOP A i,k S B i,j C j,k NP VP . PRP VBD NP PP . w i . . . w j − 1 w j . . . w k − 1 I saw DT NN IN NP unit instance of node A the boy with DT NN a telescope Forest Algorithms 37

  21. Approximate Decoding • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.0 + 0.5 4.0 + 5.0 9.0 + 0.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.1 + 0.3 4.1 + 5.4 9.1 + 0.3 3.5 4.5 + 0.6 6.5 + 10.5 11.5 + 0.6 Forest Algorithms 38

  22. Approximate Decoding • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.0 + 0.5 4.0 + 5.0 9.0 + 0.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.1 + 0.3 4.1 + 5.4 9.1 + 0.3 3.5 4.5 + 0.6 6.5 + 10.5 11.5 + 0.6 Forest Algorithms 38

  23. Approximate Decoding • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.5 9.0 9.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.4 9.5 9.4 3.5 5.1 17.0 12.1 Forest Algorithms 39

  24. Algorithm 2 => Cube Pruning • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.5 9.0 9.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.4 9.5 9.4 3.5 5.1 17.0 12.1 Forest Algorithms 40

  25. Algorithm 2 => Cube Pruning • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.5 9.0 9.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.4 9.5 9.4 3.5 5.1 17.0 12.1 Forest Algorithms 41

  26. Algorithm 2 => Cube Pruning • bottom-up, keeps top k derivations at each node • non-monotonic grid due to non-local features w ・ f N ( ) = 0.5 A i,k 1.0 3.0 8.0 B i,j C j,k 1.0 2.5 9.0 9.5 w i . . . w j − 1 w j . . . w k − 1 1.1 2.4 9.5 9.4 3.5 5.1 17.0 12.1 Forest Algorithms 42

  27. Algorithm 2 => Cube Pruning • process all hyperedges simultaneously! significant savings of computation VP hyperedge PP 1, 3 VP 3, 6 PP 1, 4 VP 4, 6 NP 1, 2 VP 2, 3 PP 3, 6 there are search errors, but the trade-off is favorable. Forest Algorithms 43

  28. Forest vs. k -best Oracles • on top of Charniak parser (modified to dump forest) • forests enjoy higher oracle scores than k -best lists • with much smaller sizes 98.6 97.8 97.2 96.7 Forest Algorithms 44

  29. Forest vs. k -best Oracles • on top of Charniak parser (modified to dump forest) • forests enjoy higher oracle scores than k -best lists • with much smaller sizes 98.6 97.8 97.2 96.7 Forest Algorithms 44

  30. Main Results • forest reranking beats 50-best & 100-best reranking • can be trained on the whole treebank in ~1 day even with a pure Python implementation! • most previous work only scaled to short sentences (<=15 words) and local features baseline: 1-best Charniak parser 89.72 feature extract approach training time F1% space time 50-best reranking 4 x 0.3h 91.43 2.4G 19h 100-best reranking 4 x 0.7h 91.49 5.3G 44h forest reranking 4 x 6.1h 91.69 1.2G 2.9h Forest Algorithms 45

  31. Main Results • forest reranking beats 50-best & 100-best reranking • can be trained on the whole treebank in ~1 day even with a pure Python implementation! • most previous work only scaled to short sentences (<=15 words) and local features baseline: 1-best Charniak parser 89.72 feature extract approach training time F1% space time 50-best reranking 4 x 0.3h 91.43 2.4G 19h 100-best reranking 4 x 0.7h 91.49 5.3G 44h forest reranking 4 x 6.1h 91.69 1.2G 2.9h Forest Algorithms 45

  32. Comparison with Others type system F 1 % Collins (2000) 89.7 n -best Charniak and Johnson (2005) 91.0 reranking updated (2006) 91.4 D Petrov and Klein (2008) 88.3 dynamic this work 91.7 programming Carreras et al. (2008) 91.1 Bod (2000) 90.7 G Petrov and Klein (2007) 90.1 semi- S McClosky et al. (2006) 92.1 supervised best accuracy to date on the Penn Treebank, and fast training Forest Algorithms 46

  33. on to Machine Translation... applying the same ideas of non-locality...

  34. Translate Server Error Forest Algorithms 48

  35. Translate Server Error clear evidence that MT is used in real life. Forest Algorithms 48

  36. Context in Translation Forest Algorithms 49

  37. Context in Translation Algorithm 2 => cube pruning fluency problem ( n -gram) Forest Algorithms 49

  38. Context in Translation xiaoxin 小心 X <=> be careful not to X Algorithm 2 => cube pruning fluency problem ( n -gram) syntax problem (SCFG) Forest Algorithms 49

  39. Context in Translation xiaoxin gou 小心 狗 <=> be aware of dog xiaoxin 小心 X <=> be careful not to X Algorithm 2 => cube pruning fluency problem ( n -gram) syntax problem (SCFG) Forest Algorithms 49

  40. Context in Translation 小心 VP <=> be careful not to VP xiaoxin gou 小心 NP <=> be careful of NP 小心 狗 <=> be aware of dog xiaoxin 小心 X <=> be careful not to X Algorithm 2 => cube pruning fluency problem ( n -gram) syntax problem (SCFG) Forest Algorithms 49

  41. 布什 与 了 How do people translate? 1. understand the source language sentence 2. generate the target language translation 会 谈 沙 龙 举 行 juxíng huìtán Bùshí Shalóng yu le and/ Bush Sharon hold meeting [ past. ] with Forest Algorithms 50

  42. 布什 与 了 How do people translate? 1. understand the source language sentence 2. generate the target language translation 会 谈 沙 龙 举 行 juxíng huìtán Bùshí Shalóng yu le and/ Bush Sharon hold meeting [ past. ] with Forest Algorithms 50

  43. 与 了 布什 How do people translate? 1. understand the source language sentence 2. generate the target language translation 会 谈 沙 龙 举 行 juxíng huìtán Bùshí Shalóng yu le and/ Bush Sharon hold meeting [ past. ] with “Bush held a meeting with Sharon” Forest Algorithms 50

  44. How do compilers translate? 1. parse high-level language program into a syntax tree 2. generate intermediate or machine code accordingly x3 = y + 3; Forest Algorithms 51

  45. How do compilers translate? 1. parse high-level language program into a syntax tree 2. generate intermediate or machine code accordingly x3 = y + 3; Forest Algorithms 51

  46. How do compilers translate? 1. parse high-level language program into a syntax tree 2. generate intermediate or machine code accordingly x3 = y + 3; LD R1, id2 ADDF R1, R1, #3.0 // add float RTOI R2, R1 // real to int ST id1, R2 Forest Algorithms 51

  47. How do compilers translate? 1. parse high-level language program into a syntax tree 2. generate intermediate or machine code accordingly x3 = y + 3; LD R1, id2 ADDF R1, R1, #3.0 // add float RTOI R2, R1 // real to int ST id1, R2 syntax-directed translation (~1960) Forest Algorithms 51

  48. Syntax-Directed Machine Translation • get 1-best parse tree; then convert to English and/ Bush Sharon hold [ past. ] meeting with “Bush held a meeting with Sharon” Forest Algorithms 52

  49. Syntax-Directed Machine Translation • recursive rewrite by pattern-matching (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 53

  50. Syntax-Directed Machine Translation • recursive rewrite by pattern-matching (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 53

  51. Syntax-Directed Machine Translation • recursive rewrite by pattern-matching (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 53

  52. Syntax-Directed Machine Translation • recursive rewrite by pattern-matching with (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 53

  53. Syntax-Directed Machine Translation • recursively solve unfinished subproblems with (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 54

  54. Syntax-Directed Machine Translation • recursively solve unfinished subproblems with (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 54

  55. Syntax-Directed Machine Translation • recursively solve unfinished subproblems Bush with (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 54

  56. Syntax-Directed Machine Translation • recursively solve unfinished subproblems Bush with held (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 54

  57. Syntax-Directed Machine Translation • continue pattern-matching Bush held with (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 55

  58. Syntax-Directed Machine Translation • continue pattern-matching Bush held with a meeting Sharon (Galley et al. 2004; Liu et al., 2006; Huang, Knight, Joshi 2006) Forest Algorithms 55

  59. Syntax-Directed Machine Translation • continue pattern-matching Bush held a meeting with Sharon Forest Algorithms 56

  60. Syntax-Directed Machine Translation • continue pattern-matching Bush held a meeting with Sharon this method is simple, fast, and expressive. but... crucial difference between PL and NL: ambiguity! using 1-best parse causes error propagation! idea: use k -best parses? use a parse forest! Forest Algorithms 56

  61. Forest-based Translation “and” / “with” Forest Algorithms 57

  62. Forest-based Translation pattern-matching on forest “and” / “with” Forest Algorithms 58

  63. Forest-based Translation pattern-matching on forest “and” “and” / “with” Forest Algorithms 58

  64. Forest-based Translation pattern-matching on forest “and” “and” / “with” Forest Algorithms 58

  65. Forest-based Translation pattern-matching on forest “and” “and” / “with” Forest Algorithms 58

  66. Forest-based Translation pattern-matching on forest “and” “and” / “with” Forest Algorithms 58

  67. Forest-based Translation pattern-matching on forest “and” “and” / “with” Forest Algorithms 58

  68. Forest-based Translation pattern-matching on forest directed by underspecified syntax “and” “and” / “with” Forest Algorithms 58

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