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Parsing German with Latent Variable Grammars Slav Petrov and Dan Klein UC Berkeley

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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?

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Previous Work: Manual Annotation Manually split categories NP: subject vs object DT: determiners vs demonstratives IN: sentential vs prepositional Advantages: Fairly compact grammar Linguistic motivations Disadvantages: Performance leveled out Manually annotated [Klein & Manning 03] ModelF1 Naïve Treebank Grammar72.6 Klein & Manning

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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. [Matsuzaki et. al 05, Prescher 05] ModelF1 Klein & Manning Matsuzaki et al

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[Petrov, Barrett, Thibaux & Klein in ACL06] [Petrov & Klein in NAACL07] Overview Learning: Hierarchical Training Adaptive Splitting Parameter Smoothing Inference: Coarse-To-Fine Decoding Variational Approximation German Analysis

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Forward Learning Latent Annotations EM algorithm: X1X1 X2X2 X7X7 X4X4 X5X5 X6X6 X3X3 Hewasright. Brackets are known Base categories are known Only induce subcategories Just like Forward-Backward for HMMs. Backward

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Starting Point Limit of computational resources

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Refinement of the DT tag DT-1 DT-2 DT-3 DT-4 DT

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Refinement of the DT tag DT

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Hierarchical Refinement of the DT tag DT

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Hierarchical Estimation Results ModelF1 Baseline87.3 Hierarchical Training88.4

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Refinement of the, tag Splitting all categories the same amount is wasteful:

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The DT tag revisited Oversplit?

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Adaptive Splitting Want to split complex categories more Idea: split everything, roll back splits which were least useful

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Adaptive Splitting Want to split complex categories more Idea: split everything, roll back splits which were least useful

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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.

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Adaptive Splitting Results ModelF1 Previous88.4 With 50% Merging89.5

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Number of Phrasal Subcategories

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Number of Lexical Subcategories

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Smoothing Heavy splitting can lead to overfitting Idea: Smoothing allows us to pool statistics

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Linear Smoothing

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ModelF1 Previous89.5 With Smoothing90.7 Result Overview

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Coarse-to-Fine Parsing [Goodman 97, Charniak&Johnson 05] Coarse grammar NP … VP NP-dog NP-cat NP-apple VP-run NP-eat… Refined grammar … Treebank Parse Prune NP-17 NP-12 NP-1 VP-6 VP-31… Refined grammar … Parse

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Hierarchical Pruning Consider the span 5 to 12: …QPNPVP… coarse: split in two: …QP1QP2NP1NP2VP1VP2… …QP1 QP3QP4NP1NP2NP3NP4VP1VP2VP3VP4… split in four: split in eight: ……………………………………………

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Intermediate Grammars X-Bar= G 0 G= G1G2G3G4G5G6G1G2G3G4G5G6 Learning DT 1 DT 2 DT 3 DT 4 DT 5 DT 6 DT 7 DT 8 DT 1 DT 2 DT 3 DT 4 DT 1 DT DT 2

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State Drift (DT tag) some this That these Thatthissome the these thissome that Thatthissome the these thissome that …………………………………………some thesethisThatThisthat EM

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G1G2G3G4G5G6G1G2G3G4G5G6 Learning G1G2G3G4G5G6G1G2G3G4G5G6 Projected Grammars X-Bar= G 0 G= Projection i 0 (G) 1 (G) 2 (G) 3 (G) 4 (G) 5 (G) G

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Bracket Posteriors (after G 0 )

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Bracket Posteriors (after G 1 )

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Bracket Posteriors (Movie)(Final Chart)

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Bracket Posteriors (Best Tree)

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Parse Selection Computing most likely unsplit tree is NP-hard: Settle for best derivation. Rerank n-best list. Use alternative objective function / Variational Approximation. Parses: -2 Derivations:

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Efficiency Results Berkeley Parser: 15 min Implemented in Java Charniak & Johnson 05 Parser 19 min Implemented in C

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Accuracy Results 40 words F1 all F1 ENG Charniak&Johnson 05 (generative) This Work GER Dubey This Work CHN Chiang et al This Work

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Parsing German Shared Task Two Pass Parsing Determine constituency structure (F1: 85/94) Assign grammatical functions One Pass Approach Treat categories+grammatical functions as labels

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Parsing German Shared Task Two Pass Parsing Determine constituency structure Assign grammatical functions One Pass Approach Treat categories+grammatical functions as labels

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Development Set Results

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Shared Task Results

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Part-of-speech splits

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Linguistic Candy

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Conclusions Split & Merge Learning Hierarchical Training Adaptive Splitting Parameter Smoothing Hierarchical Coarse-to-Fine Inference Projections Marginalization Multi-lingual Unlexicalized Parsing

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Thank You! Parser is avaliable at

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