Learning Accurate, Compact, and Interpretable Tree Annotation
Learning Accurate, Compact, and Interpretable Tree Annotation Slav Petrov, Leon Barrett, Romain Thibaux, Dan Klein The Game of Designing a Grammar Annotation refines base treebank symbols to improve statistical fit of the grammar Parent annotation [Johnson 98] The Game of Designing a Grammar Annotation refines base treebank symbols to improve statistical fit of the grammar Parent annotation [Johnson 98] Head lexicalization [Collins 99, Charniak 00] The Game of Designing a Grammar Annotation refines base treebank symbols to improve statistical fit of the grammar Parent annotation [Johnson 98] Head lexicalization [Collins 99, Charniak 00] Automatic clustering? Previous Work: Manual Annotation
[Klein & Manning 03] Manually split categories NP: subject vs object DT: determiners vs demonstratives IN: sentential vs prepositional Advantages: Fairly compact grammar Linguistic motivations Disadvantages: F1 Performance leveled out Model Nave Treebank Grammar 72.6 Manually annotated Klein & Manning 03 86.3 [Matsuzaki et. al 05, Prescher 05] Previous Work: Automatic Annotation Induction Advantages: Automatically learned: Label all nodes with latent variables. Same number k of subcategories for all categories.
Disadvantages: Grammar gets too large Most categories are oversplit while others are undersplit. Model F1 Klein & Manning 03 86.3 Matsuzaki et al. 05 86.7 Previous work is complementary Manual Annotation This Work Automatic Annotation Allocates splits where needed Very tedious Compact Grammar Misses Features Splits uniformly Automatically learned Large Grammar Captures many features
Learning Latent Annotations Forward EM algorithm: Brackets are known Base categories are known Only induce subcategories X1 X2 X3 X7 X4 X5 X6 . Just like Forward-Backward for HMMs. He was right
60 50 250 450 650 850 1050 1250 Total Number of grammar symbols 1450 1650 Refinement of the DT tag DT DT-1 DT-2 DT-3
DT-4 Refinement of the DT tag DT Hierarchical refinement of the DT tag Hierarchical Estimation Results 90 Parsing accuracy (F1) 88 86 84 82 80 78 76 74 100 300 500 700 900
Model 1100 1300 Baseline Total Number of grammar symbols 1500 1700 F1 87.3 Hierarchical Training 88.4 Refinement of the , tag Splitting all categories the same amount is wasteful: The DT tag revisited Oversplit? Adaptive Splitting Want to split complex categories more Idea: split everything, roll back splits which
were least useful Adaptive Splitting Want to split complex categories more Idea: split everything, roll back splits which were least useful Adaptive Splitting Want to split complex categories more Idea: split everything, roll back splits which were least useful Adaptive Splitting Evaluate loss in likelihood from removing each split = Data likelihood with split reversed Data likelihood with split No loss in accuracy when 50% of the splits are reversed. Adaptive Splitting Results 90 Parsing accuracy (F1) 88 86 84 82 80
50% Merging 78 Hierarchical Training 76 Flat Training 74 100 300 500 700 900 Model 1100 1300 Total Number of grammar Previous symbols
1500 1700 F1 88.4 With 50% Merging 89.5 0 LST ROOT X WHADJP RRC SBARQ INTJ WHADVP UCP NAC
78 58 34 Conclusions New Ideas: Hierarchical Training Adaptive Splitting Parameter Smoothing State of the Art Parsing Performance: Improves from X-Bar initializer 63.4 to 90.2 Linguistically interesting grammars to sift through. Thank You! [email protected] Other things we tried X-Bar vs structurally annotated grammar: X-Bar grammar starts at lower performance, but provides more flexibility Better Smoothing: Tried different (hierarchical) smoothing methods, all worked about the same
(Linguistically) constraining rewrite possibilities between subcategories: Hurts performance EM automatically learns that most subcategory combinations are meaningless: 90% of the possible rewrites have 0 probability
EECS 262a Advanced Topics in Computer SystemsLecture 10Transactions and Isolation Levels (Con't)February 24th, 2016. John Kubiatowicz. Slides by Alan Fekete (University of Sydney), Anthony D. Joseph and John Kubiatowicz (UC Berkeley)
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