1. Paris 2008 Treebank-Based LFG Resources 1 Treebank-Based Wide Coverage Probabilistic LFG Resources Josef van Genabith, Aoife Cahill, Grzegorz Chrupala, Jennifer Foster, Deirdre Hogan, Conor Cafferkey, Mick Burke, Ruth O’Donovan, Yvette Graham, Karolina Owczarzak, Yuqing Guo, Ines Rehbein, Natalie Schluter and Djame Sedah National Centre for Language Technology NCLT School of Computing, Dublin City University

2. Paris 2008 Treebank-Based LFG Resources 2 Overview Context/Motivation Treebank-Based Acquisition of Wide-Coverage LFG Resources (Penn-II) –LFG –Automatic F-Structure Annotation Algorithm –Acquisition of Lexical Resources Parsing –Parsing Architectures –LDD-Resolution –Comparison with Hand-Crafted (XLE, RASP) and Treebank-Based (CCG, HPSG) Resources Generation –Basic Generator –Generation Grammar Transforms –“ History-Based ” Generation MT Evaluation

3. Paris 2008 Treebank-Based LFG Resources 3 Motivation What do grammars do? –Grammars define languages as sets of strings –Grammars define what strings are grammatical and what strings are not –Grammars tell us about the syntactic structure of (associated with) strings “Shallow” vs. “Deep” grammars Shallow grammars do all of the above Deep grammars (in addition) relate text to information/meaning representation Information: predicate-argument-adjunct structure, deep dependency relations, logical forms, … In natural languages, linguistic material is not always interpreted locally where you encounter it: long-distance dependencies (LDDs) Resolution of LDDs crucial to construct accurate and complete information/meaning representations. Deep grammars := (text meaning) + (LDD resolution)

4. Paris 2008 Treebank-Based LFG Resources 4 Motivation Constraint-Based Grammar Formalisms (FU, GPSG, PATR-II, …) –Lexical-Functional Grammar (LFG) –Head-Driven Phrase Structure Grammar (HPSG) –Combinatory Categorial Grammar (CCG) –Tree-Adjoining Grammar (TAG) Traditionally, deep constraint-based grammars are hand-crafted LFG ParGram, HPSG LingoErg, Core Language Engine CLE, Alvey Tools, RASP, ALPINO, … Wide-coverage, deep constraint-based grammar development is very time consuming, knowledge extensive and expensive! Very hard to scale hand-crafted grammars to unrestricted text! English XLE (Riezler et al. 2002); German XLE (Forst and Rohrer 2006); Japanese XLE (Masuichi and Okuma 2003); RASP (Carroll and Briscoe 2002); ALPINO (Bouma, van Noord and Malouf, 2000)

5. Paris 2008 Treebank-Based LFG Resources 5 Motivation Instance of “knowledge acquisition bottleneck” familiar from classical “rationalist rule/knowledge-based” AI/NLP Alternative to classical “rationalist” rule/knowledge-based AI/NLP “Empiricist data-driven ” research paradigm (AI/NLP): –Corpora, …, machine-learning-based and statistical approaches, … –Treebank-based grammar acquisition, probabilistic parsing –Advantage: grammars can be induced (learned) automatically –Very low development cost, wide-coverage, robust, but … Most treebank-based grammar induction/parsing technology produces “shallow” grammars Shallow grammars don’t resolve LDDs (but see (Johnson 2002); …), do not map strings to information/meaning representations …

6. Paris 2008 Treebank-Based LFG Resources 6 Motivation Poses a number of research questions: Can we address the knowledge acquisition bottleneck for deep grammar development by combining insights from rationalist and empiricist research paradigms? Specifically: Can we automatically acquire wide-coverage “deep”, probabilistic, constraint-based grammars from treebanks? How do we use them in parsing? Can we use them for generation? Can we acquire resources for different languages and treebank encodings? How do these resources compare with hand-crafted resources? How do they fare in applications … ?

7. Paris 2008 Treebank-Based LFG Resources 7 Context TAG (Xia, 2001) LFG (Cahill, McCarthy, van Genabith and Way, 2002) CCG (Hockenmaier & Steedman, 2002) HPSG (Miyao and Tsujii, 2003) LFG (van Genabith, Sadler and Way, 1999) (Frank, 2000) (Sadler, van Genabith and Way, 2000) (Frank, Sadler, van Genabith and Way, 2003)

8. Paris 2008 Treebank-Based LFG Resources 8 Lexical-Functional Grammar (LFG) Parsing

9. Paris 2008 Treebank-Based LFG Resources 9 LFG Acquisition for English - Overview Treebank-Based Acquisition of LFG Resources (Penn-II) –Lexical Functional Grammar LFG –Penn-II Treebank & Preprocessing/Clean-Up –F-Str Annotation Algorithm –Grammar and Lexicon Extraction Parsing Architectures (LDD Resolution) Comparison with best hand-crafted resources: XLE and RASP Comparison with treebank-based CCG and HPSG resources

10. Paris 2008 Treebank-Based LFG Resources 10 Lexical-Functional Grammar (LFG) Lexical-Functional Grammar (LFG) (Bresnan & Kaplan 1981, Bresnan 2001, Dalrymple 2001) is a constraint-based theory of grammar. Two (basic) levels of representation: C-structure: represents surface grammatical configurations such as word order, annotated CFG rules/trees F-structure: represents abstract syntactic functions such as SUBJ(ject), OBJ(ect), OBL(ique), PRED(icate), COMP(lement), ADJ(unct) …, AVM attribute-value matrices/feature structures F-structure approximates to basic predicate-argument structure, dependency representation, logical form (van Genabith and Crouch, 1996; 1997)

11. Paris 2008 Treebank-Based LFG Resources 11 Lexical-Functional Grammar (LFG)

12. Paris 2008 Treebank-Based LFG Resources 12 Lexical-Functional Grammar (LFG) Subcategorisation: –Semantic forms (subcat frames): see –Completeness: all GFs in semantic form present at local f-structure –Coherence: only the GFs in semantic form present at local f- structure Long Distance Dependencies (LDDs): resolved at f-structure with –Functional Uncertainty Equations (regular expressions specifying paths in f-structure): e.g.  TOPICREL =  COMP * OBJ –subcat frames –Completeness/Coherence.

13. Paris 2008 Treebank-Based LFG Resources 13 Lexical-Functional Grammar (LFG)

14. Paris 2008 Treebank-Based LFG Resources 14 Introduction: Penn-II & LFG If we had f-structure annotated version of Penn-II, we could use (standard) machine learning methods to extract probabilistic, wide- coverage LFG resources How do we get f-structure annotated Penn-II? Manually? No: ~50,000 trees … ! Automatically! Yes: F-Structure annotation algorithm … ! Penn-II is a 2 nd generation treebank – contains lots of annotations to support derivation of deep meaning representations: –trees, Penn-II “ functional ” tags ( -SBJ, -TMP, -LOC ), traces & coindexation f-structure annotation algorithm exploits those.

15. Paris 2008 Treebank-Based LFG Resources 15 Treebank Annotation: Penn-II & LFG

16. Paris 2008 Treebank-Based LFG Resources 16 Treebank Annotation: Penn-II & LFG

17. Paris 2008 Treebank-Based LFG Resources 17 Treebank Preprocessing/Clean-Up: Penn-II & LFG Penn-II treebank: often flat analyses (coordination, NPs …), a certain amount of noise: inconsistent annotations, errors … No treebank preprocessing or clean-up in the LFG approach (unlike CCG- and HPSG-based approaches) –Take Penn-II treebank as is, but –Remove all trees with FRAG or X labelled constituents –Frag = fragments, X = not known how to annotate Total of 48,424 trees as they are.

18. Paris 2008 Treebank-Based LFG Resources 18 Treebank Annotation: Penn-II & LFG Annotation-based (rather than conversion-based) Automatic annotation of nodes in Penn-II treebank trees with f- structure equations Annotation Algorithm exploits: –Head information –Categorial information –Configurational information –Penn-II functional tags –Trace information

19. Paris 2008 Treebank-Based LFG Resources 19 Treebank Annotation: Penn-II & LFG Architecture of a modular algorithm to assign LFG f-structure equations to trees in the Penn-II treebank: Left-Right Context Annotation Principles Coordination Annotation Principles Catch-All and Clean-Up Traces Proto F-Structures Proper F-Structures Head-Lexicalisation [Magerman,1994]

20. Paris 2008 Treebank-Based LFG Resources 20 Treebank Annotation: Penn-II & LFG Head Lexicalisation: modified rules based on (Magerman, 1994)

21. Paris 2008 Treebank-Based LFG Resources 21 Treebank Annotation: Penn-II & LFG Left-Right Context Annotation Principles: Head of NP likely to be rightmost noun … Mother → Left Context Head Right Context Left Context Right Context Head

22. Paris 2008 Treebank-Based LFG Resources 22 Treebank Annotation: Penn-II & LFG Left ContextHeadRight Context DT: ↑ spec:det= ↓ QP: ↑ spec:quant= ↓ JJ, ADJP: ↓  ↑ adjunct NN, NNS: ↑ = ↓ NP: ↓  ↑ app PP: ↓  ↑ adjunct S, SBAR: ↓  ↑ relmod NP DT RB ADJP very politicized NN JJdeala NP ↑ spec:det= ↓ DT RB ↓  ↑ adjunct ADJP very politicized ↑ = ↓ NN JJdeala → NP: Left-Right Annotation Matrix

23. Paris 2008 Treebank-Based LFG Resources 23 Treebank Annotation: Penn-II & LFG

24. Paris 2008 Treebank-Based LFG Resources 24 Treebank Annotation: Penn-II & LFG Do annotation matrix for each of the monadic categories (without –Fun tags) in Penn-II Based on analysing the most frequent rule types for each category such that  sum total of token frequencies of these rule types is greater than 85% of total number of rule tokens for that category 100% 85% 100% 85%  NP 6595 102 VP 10239 307  S 2602 20 ADVP 234 6 Apply annotation matrix to all (i.e. also unseen) rules/sub-trees, i.e. also those NP -LOC, NP -TMP etc.

25. Paris 2008 Treebank-Based LFG Resources 25 Treebank Annotation: Penn-II & LFG Traces Module: Long Distance Dependencies (LDDs) Topicalisation Questions Wh- and wh-less relative clauses Passivisation Control constructions ICH (interpret constituent here) RNR (right node raising) … Translate Penn-II traces and coindexation into corresponding reentrancy in f-structure

26. Paris 2008 Treebank-Based LFG Resources 26 Treebank Annotation: Control & Wh-Rel. LDD

27. Paris 2008 Treebank-Based LFG Resources 27 Treebank Annotation: Penn-II & LFG Left-Right Context Annotation Principles Coordination Annotation Principles Catch-All and Clean-Up Traces Proto F-Structures Proper F-Structures Head-Lexicalisation [Magerman,1995] Constraint Solver

28. Paris 2008 Treebank-Based LFG Resources 28 Treebank Annotation: Penn-II & LFG Collect f-structure equations Send to constraint solver Generates f-structures F-structure annotation algorithm in Java, constraint solver in Prolog ~3 min annotating ~50,000 Penn-II trees ~5 min producing ~50,000 f-structures

29. Paris 2008 Treebank-Based LFG Resources 29 Evaluation (Quantitative): Coverage: Over 99.8% of Penn-II sentences (without X and FRAG constituents) receive a single covering and connected f-structure: Treebank Annotation: Penn-II & LFG 0 F-structures 450.093% 1 F-structure4832999.804% 2 F-structures 500.103%

30. Paris 2008 Treebank-Based LFG Resources 30 Treebank Annotation: Penn-II & LFG F-structure quality evaluation against DCU 105 Dependency Bank, a manually annotated dependency gold standard of 105 sentences randomly extracted from WSJ section 23. Triples are extracted from the gold standard Evaluation software from (Crouch et al. 2002) and (Riezler et al. 2002) relation(predicate~0, argument~1) DCU 105All AnnotationsPreds-Only Precision 97.06% 94.28% Recall 96.80% 94.28%

31. Paris 2008 Treebank-Based LFG Resources 31 Treebank Annotation: Penn-II & LFG Following (Kaplan et al. 2004) evaluation against PARC 700 Dependency Bank calculated for: all annotations  PARC features  preds-only Mapping required (Burke 2004, 2006) PARC 700PARC features Precision 88.31% Recall 86.38%

32. Paris 2008 Treebank-Based LFG Resources 32 Grammar and Lexicon Extraction : Penn-II & LFG Lexical Resources: Lexical information extremely important in modern lexicalised grammar formalisms LFG, HPSG, CCG, TAG, … Lexicon development is time consuming and extremely expensive Rarely if ever complete Familiar knowledge acquisition bottleneck … Treebank-based subcategorisation frame induction (LFG semantic forms) from Penn-II and –III Parser-based induction from British National Corpus (BNC) Evaluation against COMLEX, OALD, Korhonen’s data set

33. Paris 2008 Treebank-Based LFG Resources 33 Grammar and Lexicon Extraction: Penn-II & LFG Lexicon Construction –Manual vs. Automated Our Approach: – Subcat Frames not Predefined – Functional and/or Categorial Information – Parameterised for Prepositions and Particles – Active and Passive – Long Distance Dependencies – Conditional Probabilities

34. Paris 2008 Treebank-Based LFG Resources 34 Grammar and Lexicon Extraction: Penn-II & LFG

35. Paris 2008 Treebank-Based LFG Resources 35 Grammar and Lexicon Extraction: Penn-II & LFG apply win

36. Paris 2008 Treebank-Based LFG Resources 36 Grammar and Lexicon Extraction: Penn-II & LFG Lexicon extracted from Penn-II (O’Donovan et al 2005):

37. Paris 2008 Treebank-Based LFG Resources 37 Grammar and Lexicon Extraction: Penn-II & LFG

38. Paris 2008 Treebank-Based LFG Resources 38 Grammar and Lexicon Extraction: Penn-II & LFG Parsing-Based Subcat Frame Extraction (O’Donovan 2006): Treebank-based vs. parsing-based subcat frame extraction Parsed British National Corpus BNC (100 million words) with our automatically induced LFGs 19 days on single machine: ~5 million words per day Subcat frame extraction for ~10,000 verb lemmas Evaluation against COMLEX and OALD Evaluation against Korhonen (2002) gold standard Our method is statistically significantly better than Korhonen (2002)

39. Paris 2008 Treebank-Based LFG Resources 39 Parsing: Penn-II and LFG Overview Parsing Architectures: Pipeline & Integrated Long-Distance Dependency (LDD) Resolution at F-Structure Evaluation & Comparison with Hand-Crafted Resources (XLE and RASP) Comparison against Treebank-Based CCG and HPSG Resources

40. Paris 2008 Treebank-Based LFG Resources 40 Parsing: Penn-II and LFG

41. Paris 2008 Treebank-Based LFG Resources 41 Lexical-Functional Grammar (LFG)

42. Paris 2008 Treebank-Based LFG Resources 42 Parsing: Penn-II and LFG Require: –subcategorisation frames (O’Donovan et al., 2004, 2005; O’Donovan 2006) –functional uncertainty equations Previous Example: –claim([subj,comp]), deny([subj,obj]) –  topicrel =  comp* obj (search along a path of 0 or more comps)

43. Paris 2008 Treebank-Based LFG Resources 43 Parsing: Penn-II and LFG Subcat frames: as above (O’Donovan et al. 2004, 2005) Functional Uncertainty equations: Automatically acquire finite approximations of FU-equations Extract paths between co-indexed material in automatically generated f- structures from sections 02-21 from Penn-II 26 TOPIC, 60 TOPICREL, 13 FOCUS path types 99.69% coverage of paths in WSJ Section 23 Each path type associated with a probability LDD resolution ranked by Path x Subcat probabilities (Cahill et al., 2004)

44. Paris 2008 Treebank-Based LFG Resources 44 Parsing: Penn-II and LFG How do treebank-based constraint grammars compare to deep hand- crafted grammars like XLE and RASP? XLE (Riezler et al. 2002, Kaplan et al. 2004) –hand-crafted, wide-coverage, deep, state-of-the-art English LFG and XLE parsing system with log-linear-based probability models for disambiguation –PARC 700 Dependency Bank gold standard (King et al. 2003), Penn-II Section 23-based RASP (Carroll and Briscoe 2002) –hand-crafted, wide-coverage, deep, state-of-the-art English probabilistic unification grammar and parsing system (RASP Rapid Accurate Statistical Parsing) –CBS 500 Dependency Bank gold standard (Carroll, Briscoe and Sanfillippo 1999), Susanne-based

45. Paris 2008 Treebank-Based LFG Resources 45 (Bikel 2002) retrained to retain Penn-II functional tags (-SBJ, -SBJ, -LOC,- TMP, -CLR, -LGS, etc.) Pipeline architecture: tag text  Bikel retrained + f-structure annotation algorithm + LDD resolution  f-structures  automatic conversion  evaluation against XLE/RASP gold standards PARC-700/CBS-500 Dependency Banks Parsing: Penn-II and LFG

46. Paris 2008 Treebank-Based LFG Resources 46 Systematic differences between f-structures and PARC 700 and CBS 500 dependency representations Automatic conversion of f-structures to PARC 700 / CBS 500 -like structures (Burke et al. 2004, Burke 2006, Cahill et al. 2008) Evaluation software (Crouch et al. 2002) and (Carroll and Briscoe 2002) Approximate Randomisation Test (Noreen 1989) for statistical significance Parsing: Penn-II and LFG

47. Paris 2008 Treebank-Based LFG Resources 47 Parsing: Penn-II and LFG Result dependency f-scores (CL 2008 paper): PARC 700 XLE vs. DCU-LFG 80.55% XLE 82.73% DCU-LFG (+2.18%) CBS 500 RASP vs. DCU-LFG 76.57% RASP 80.23% DCU-LFG (+3.66%) Results statistically significant at  95% level (Noreen 1989) Best result now against PARC 700 84.00% (+3.45%) Charniak + Reranker + Grzegorz’ Penn-II function-tag labeler

48. Paris 2008 Treebank-Based LFG Resources 48 Parsing: Penn-II and LFG PARC 700 Evaluation:

49. Paris 2008 Treebank-Based LFG Resources 49 Parsing: Penn-II and LFG

50. Paris 2008 Treebank-Based LFG Resources 50 Parsing: Penn-II and LFG

51. Paris 2008 Treebank-Based LFG Resources 51 Parsing: Penn-II and LFG

52. Paris 2008 Treebank-Based LFG Resources 52 Parsing: Penn-II and LFG

53. Paris 2008 Treebank-Based LFG Resources 53 Parsing: Penn-II and LFG

54. Paris 2008 Treebank-Based LFG Resources 54 Evaluation against Treebank-Based CCG and HPSG CCG = Combinatory Categorial Grammar (Steedman 2000) HPSG = Head-Driven Phrase Structure Grammar (Pollard & Sag 1994) –Both constraint-based grammar formalisms –Treebank-based CCG resources (Hockenmaier & Steedman 2002, Hockenmaier 2003, Clark & Curran 2004, …) –Treebank-based HPSG resources (Miyao, Ninomiya & Tsujii 2003, Miyao & Tsujii 2004, …) DepBank = reannotated version of PARC 700 (Briscoe & Carroll 2006) with CBS 500–style GRs RASP (version 2) (Briscoe & Carroll 2006)

55. Paris 2008 Treebank-Based LFG Resources 55 Evaluation against Treebank-Based CCG and HPSG CCG: –Small set of basic categories: { NP, N, PP, S } –Complex categories: VP = S\NP V i = S\NP V di = (S\NP)/NP –Small set of combination rules: X/Y Y  X Y X\Y  X X/Y Y/Z  X/Z …

56. Paris 2008 Treebank-Based LFG Resources 56 Evaluation against Treebank-Based CCG and HPSG HPSG: –Uniform representation: typed feature structures and inheritance –Sign: PHON, SYNSEM, DTRS –Inheritance hierarchy –Principles ( HEAD-FEATURE, VALENCE, …) –Id-Schemata ( HEAD-COMP, HEAD-MOD, …)

57. Paris 2008 Treebank-Based LFG Resources 57 Evaluation against Treebank-Based CCG and HPSG

58. Paris 2008 Treebank-Based LFG Resources 58 Evaluation against Treebank-Based CCG and HPSG

59. Paris 2008 Treebank-Based LFG Resources 59 Evaluation against Treebank-Based CCG and HPSG

60. Paris 2008 Treebank-Based LFG Resources 60 Probability Models: Penn-II & LFG

61. Paris 2008 Treebank-Based LFG Resources 61 Probability Models: Penn-II & LFG Evaluation Results:

62. Paris 2008 Treebank-Based LFG Resources 62 Probability Models: Penn-II & LFG Results are interesting as: Extensive treebank preprocessing (clean-up, correction and restructuring) in CCG and (some in) HPSG none in LFG Custom-designed parsers and sophisticated (log-linear, max ent) parse selection probability models in HPSG and CCG Mix of off-the-shelf and custom designed components, each with their own probability model in early-disambiguation processing pipeline in LFG, no proper overall probability model, but an approximation at best … Still competitive results …

63. Paris 2008 Treebank-Based LFG Resources 63 Probability Models: Penn-II & LFG Probability Models: Our approach does not constitute proper probability model (Abney, 1996) Why? Probability model leaks: Highest ranking parse tree may feature f-structure equations that cannot be resolved into f-structure Probability associated with that parse tree is lost Doesn’t happen often in practice (coverage >99.5% on unseen data) Research on appropriate discriminative, log-linear or maximum entropy models is important (Miyao and Tsujii, 2002) (Riezler et al. 2002)

64. Paris 2008 Treebank-Based LFG Resources 64 Demo System http://lfg-demo.computing.dcu.ie/lfgparser.html

65. Paris 2008 Treebank-Based LFG Resources 65 Applications: Generation

66. Paris 2008 Treebank-Based LFG Resources 66 Applications: Generation Research Question: Can we make the automatically induced LFG resources reversible/bi- directional? Can they be used for both (probabilistic) parsing and generation?

67. Paris 2008 Treebank-Based LFG Resources 67 Generation: Penn-II & LFG

68. Paris 2008 Treebank-Based LFG Resources 68 Generation: Penn-II & LFG

69. Paris 2008 Treebank-Based LFG Resources 69 Generation: Penn-II & LFG

70. Paris 2008 Treebank-Based LFG Resources 70 Generation: Penn-II & LFG

71. Paris 2008 Treebank-Based LFG Resources 71 Generation: Penn-II & LFG

72. Paris 2008 Treebank-Based LFG Resources 72 Generation: Penn-II & LFG

73. Paris 2008 Treebank-Based LFG Resources 73 Generation: Penn-II & LFG

74. Paris 2008 Treebank-Based LFG Resources 74 Generation: Penn-II & LFG Problem: conditioning of generation rules on purely local f-str features Solution I: generation grammar transformation (Cahill et al. 2006) Solution II: history-based probabilistic generation (Hogan et al. 2007, Cafferkey et al. 2007): condition generation rules on parent GF

75. Paris 2008 Treebank-Based LFG Resources 75 Generation: Penn-II & LFG

76. Paris 2008 Treebank-Based LFG Resources 76 Generation: Penn-II & LFG

77. Paris 2008 Treebank-Based LFG Resources 77 Generation: Penn-II & LFG

78. Paris 2008 Treebank-Based LFG Resources 78 Generation: the Good, the Bad and the Ugly Orig: Supporters of the legislation view the bill as an effort to add stability and certainty to the airline-acquisition process, and to preserve the safety and fitness of the industry. Gen: Supporters of the legislation view the bill as an effort to add stability and certainty to the airline-acquisition process, and to preserve the safety and fitness of the industry. Orig: The upshot of the downshoot is that the A 's go into San Francisco 's Candlestick Park tonight up two games to none in the best-of-seven fest. Gen: The upshot of the downshoot is that the A 's tonight go into San Francisco 's Candlestick Park up two games to none in the best-of-seven fest. Orig: By this time, it was 4:30 a.m. in New York, and Mr. Smith fielded a call from a New York customer wanting an opinion on the British stock market, which had been having troubles of its own even before Friday 's New York market break. Gen: Mr. Smith fielded a call from New a customer York wanting an opinion on the market British stock which had been having troubles of its own even before Friday 's New York market break by this time and in New York, it was 4:30 a.m.. Orig: Only half the usual lunchtime crowd gathered at the tony Corney & Barrow wine bar on Old Broad Street nearby. Gen: At wine tony Corney & Barrow the bar on Old Broad Street nearby gathered usual, lunchtime only half the crowd,.

79. Paris 2008 Treebank-Based LFG Resources 79 Generation: Penn-II & LFG

80. Paris 2008 Treebank-Based LFG Resources 80 Generation: Penn-II & LFG Problem: conditioning of generation rules on purely local f-str features Solution: generation grammar transformation (Cahill et al. 2006) Solution: history-based probabilistic generation (Hogan et al. 2007, Cafferkey et al. 2007): condition generation rules on parent GF

81. Paris 2008 Treebank-Based LFG Resources 81 Generation: the Good, the Bad and the Ugly Orig: By this time, it was 4:30 a.m. in New York, and Mr. Smith fielded a call from a New York customer wanting an opinion on the British stock market, which had been having troubles of its own even before Friday 's New York market break. Gen: Mr. Smith fielded a call from New a customer York wanting an opinion on the market British stock which had been having troubles of its own even before Friday 's New York market break by this time and in New York, it was 4:30 a.m.. (Cahill et al. 2006) GGT Gen: By this time, in New York, it was 4:30 a.m., and Mr. Smith fielded a call from New a customer York, wanting an opinion on the market British stock which had been having troubles of its own even before Friday ’s New York market break. (Hogan et al. 2007) HB Gen: By this time, in New York, it was 4:30 a.m., and Mr. Smith fielded a call from a New York customer, wanting an opinion on the market British stock which had been having troubles of its own even before Friday ’s New York market break. (Hogan et al. 2007) HB + MWU

82. Paris 2008 Treebank-Based LFG Resources 82 Generation: Chinese CTB2 CTB2 (Yuqing Guo - Toshiba China Beijing R&D Lab) (Cahill et al. 2006) out of the box Training articles 1-270 (3,480 sentences) Testing articles 301-325 (351 sentences)

83. Paris 2008 Treebank-Based LFG Resources 83 Applications: Machine Translation Labelled Dependency-Based MT Evaluation (LaDEva) Automatic Acquisition of Transfer Rules

84. Paris 2008 Treebank-Based LFG Resources 84 Applications: Machine Translation Labelled-Dependency-Based MT Evaluation Most automatic MT evaluation metrics (BLEU, NIST) are string (n-gram) based. They unfairly punish perfectly legitimate syntactic and lexical variation: Yesterday John resigned. John resigned yesterday. Yesterday John quit. Legitimate lexical variation: throw in WordNet synonyms into the string match What about syntactic variation?

85. Paris 2008 Treebank-Based LFG Resources 85 Applications: Machine Translation Idea: use labelled dependencies for MT evaluation Why: dependencies abstract away from some particulars of surface realisation Adjunct placement, order of conjuncts in a coordination, topicalisation,...

86. Paris 2008 Treebank-Based LFG Resources 86 Applications: Machine Translation Idea is intuitive To make it happen you need a robust parser that can parse MT output Treebank-induced parsers parse anything …! How do we judge whether labelled dependency-based method is better than string-based methods? We compare (correlation) with human judgement/evaluation performance … Why: humans not fooled by legitimate syntactic variation

87. Paris 2008 Treebank-Based LFG Resources 87 Applications: Machine Translation Experiment: use LDC Multiple Translation Chinese (MTC) Parts 2 and 4 data 16,807 translation-reference human score segments 5,007 test, rest for training (weights … etc.) To make this work, we throw in –n-best parsing –WordNet synonyms –partial matching –training weights –etc …

88. Paris 2008 Treebank-Based LFG Resources 88 Applications: Machine Translation

89. Paris 2008 Treebank-Based LFG Resources 89 Applications: Machine Translation

90. Paris 2008 Treebank-Based LFG Resources 90 References (MT Eval) Karolina Owczarzak, Yvette Graham and Josef van Genabith: Using F-structures in Machine Translation Evaluation. In Proceedings of the 12th International Conference on Lexical Functional Grammar, July 28-30, 2007, Stanford, CA Karolina Owczarzak, Josef van Genabith, and Andy Way. Labelled Dependencies in Machine Translation Evaluation. In Proceedings of ACL 2007 Workshop on Statistical Machine Translation, pages 104-111, Prague, Czech Republic Karolina Owczarzak, Josef van Genabith, and Andy Way. Dependency-Based Automatic Evaluation for Machine Translation. In Proceedings of HLT-NAACL 2007 Workshop on Syntax and Structure in Statistical Translation. Rochester, NY.

91. Paris 2008 Treebank-Based LFG Resources 91 References (Parsing) Aoife Cahill, Michael Burke, Ruth O'Donovan, Stefan Riezler, Josef van Genabith and Andy Way. 2008. Wide-Coverage Statistical Parsing Using Automatic Dependency Structure Annotation. Computational Linguistics, Volume 34, 1, MIT Press, March 2008. (accepted for publication) Joachim Wagner, Djamé Seddah, Jennifer Foster and Josef van Genabith: C-Structures and F-Structures for the British National Corpus. In Proceedings of the 12th International Conference on Lexical Functional Grammar, July 28-30, 2007, Stanford, CA A. Cahill, M. Burke, R. O'Donovan, J. van Genabith, and A. Way. Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), July 21-26 2004, pages 320-327, Barcelona, Spain, 2004 Cahill A, M. McCarthy, J. van Genabith and A. Way. Parsing with PCFGs and Automatic F-Structure Annotation, In M. Butt and T. Holloway-King (eds.): Proceedings of the Seventh International Conference on LFG CSLI Publications, Stanford, CA., pp.76--95. 2002

92. Paris 2008 Treebank-Based LFG Resources 92 References (Generation, Lex. Acq.) Deirdre Hogan, Conor Cafferkey, Aoife Cahill and Josef van Genabith, Exploiting Multi- Word Units in History-Based Probabilistic Generation, in Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL 2007), Prague, Czech Republic. pp.267-276 A. Cahill and J. Van Genabith, Robust PCFG-Based Generation using Automatically Acquired LFG-Approximations, COLING/ACL 2006, Sydney, Australia R. O'Donovan, M. Burke, A. Cahill, J. van Genabith and A. Way. Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II and Penn-III Treebanks, Computational Linguistics, 2005 R. O'Donovan, M. Burke, A. Cahill, J. van Genabith, and A. Way. Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II Treebank, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), July 21- 26 2004, pages 368-375, Barcelona, Spain, 2004