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Inducing Structure for Perception Slav Petrov Advisors: Dan Klein, Jitendra Malik Collaborators: L. Barrett, R. Thibaux, A. Faria, A. Pauls, P. Liang, A. Berg a.k.a. Slav’s split&merge Hammer

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The Main Idea Complex underlying process Observation Manually specified structure True structure MLE structure He was right.

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The Main Idea Complex underlying process Observation He was right. Manually specified structure Automatically refined structure EM

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Why Structure? the the the food cat dog ate and t e c a e h t g f a o d o o d n h e t d a

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Structure is important The dog ate the cat and the food. The dog and the cat ate the food. The cat ate the food and the dog.

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Syntactic Ambiguity Last night I shot an elephant in my pajamas.

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Visual Ambiguity Old or young?

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Three Peaks? Machine Learning Computer Vision Natural Language Processing

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No, One Mountain! Machine Learning Computer Vision Natural Language Processing

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Three Domains SpeechScenesSyntax

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Timeline LearningInference Syntactic MT Bayesian Conditional Summer ISI ‘07 ‘08‘09 Learning Decoding Synthesis LearningInference Syntax Scenes Speech TrecVid Now

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Syntax Speech Scenes Language Modeling Split & Merge Learning Syntactic Machine Translation Coarse-to-Fine Inference Non- parametric Bayesian Learning Generative vs. Conditional Learning Syntax

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Learning accurate, compact and interpretable Tree Annotation Slav Petrov, Leon Barrett, Romain Thibaux, Dan Klein

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Motivation (Syntax) Task: He was right. Why? Information Extraction Syntactic Machine Translation

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Treebank Treebank Parsing S NP VP.1.0 NP PRP0.5 NP DT NN0.5 … PRP She1.0 DT the1.0 … Grammar

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Non-Independence Independence assumptions are often too strong. All NPsNPs under SNPs under VP

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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]

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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]

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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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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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Inside/Outside Scores A x ByBy CzCz Inside:Outside: A x CzCz ByBy

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Learning Latent Annotations (Details) E-Step: M-Step: A x ByBy CzCz

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Overview Limit of computational resources - Hierarchical Training - Adaptive Splitting - Parameter Smoothing

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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 (Details) True data likelihood: Approximate likelihood with split at n reversed: Approximate loss in likelihood:

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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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PP VP NPNP Number of Phrasal Subcategories

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X NA C Number of Phrasal Subcategories

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TOTO, PO S Number of Lexical Subcategories

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IN DT RBRB VBx Number of Lexical Subcategories

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N NN S NN P JJ

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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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Proper Nouns (NNP): Personal pronouns (PRP): NNP-14Oct.Nov.Sept. NNP-12JohnRobertJames NNP-2J.E.L. NNP-1BushNoriegaPeters NNP-15NewSanWall NNP-3YorkFranciscoStreet PRP-0ItHeI PRP-1ithethey PRP-2itthemhim Linguistic Candy

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Relative adverbs (RBR): Cardinal Numbers (CD): RBR-0furtherlowerhigher RBR-1morelessMore RBR-2earlierEarlierlater CD-7onetwoThree CD-4198919901988 CD-11millionbilliontrillion CD-0150100 CD-313031 CD-9785834

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Nonparametric PCFGs using Dirichlet Processes Percy Liang, Slav Petrov, Dan Klein and Michael Jordan

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Improved Inference for Unlexicalized Parsing Slav Petrov and Dan Klein

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1621 min

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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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Prune? For each chart item X[i,j], compute posterior probability: …QPNPVP… coarse: refined: E.g. consider the span 5 to 12: < threshold

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1621 min 111 min (no search error)

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Hierarchical Pruning Consider again 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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1621 min 111 min 35 min (no search error)

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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)0(G)1(G)2(G)3(G)4(G)5(G) G

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Estimating Projected Grammars Nonterminals? Nonterminals in G NP 1 VP 1 VP 0 S0S0 S1S1 NP 0 Nonterminals in (G) VP S NP Projection Easy:

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Rules in G Rules in (G) Estimating Projected Grammars Rules? S 1 NP 1 VP 1 0.20 S 1 NP 1 VP 2 0.12 S 1 NP 2 VP 1 0.02 S 1 NP 2 VP 2 0.03 S 2 NP 1 VP 1 0.11 S 2 NP 1 VP 2 0.05 S 2 NP 2 VP 1 0.08 S 2 NP 2 VP 2 0.12 S NP VP ? ???

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Treebank Estimating Projected Grammars [Corazza & Satta ‘06] Rules in (G) S NP VP Rules in G S1 NP1 VP1 0.20 S1 NP1 VP2 0.12 S1 NP2 VP1 0.02 S1 NP2 VP2 0.03 S2 NP1 VP1 0.11 S2 NP1 VP2 0.05 S2 NP2 VP1 0.08 S2 NP2 VP2 0.12 Infinite tree distribution … … 0.56 Estimating Grammars

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Calculating Expectations Nonterminals: c k (X) : expected counts up to depth k Converges within 25 iterations (few seconds) Rules:

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1621 min 111 min 35 min 15 min (no search error)

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G1G2G3G4G5G6G1G2G3G4G5G6 Learning Parsing times X-Bar= G 0 G= 60 % 12 % 7 % 6 % 5 % 4 %

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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. Parses: -2 Derivations: -2 -2 -2

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Final Results (Efficiency) Berkeley Parser: 15 min 91.2 F-score Implemented in Java Charniak & Johnson ‘05 Parser 19 min 90.7 F-score Implemented in C

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Final Results (Accuracy) ≤ 40 words F1 all F1 ENG Charniak&Johnson ‘05 (generative)90.189.6 This Work90.690.1 GER Dubey ‘0576.3- This Work80.880.1 CHN Chiang et al. ‘0280.076.6 This Work86.383.4

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

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Generative vs. Discriminative Conditional Estimation L-BFGS Iterative Scaling Conditional Structure Alternative Merging Criterion

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How much supervision?

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Syntactic Machine Translation Collaboration with ISI/USC: Use parse trees Use annotated parse trees Learn split synchronous grammars

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Speech Scenes Syntax Speech Synthesis Split & Merge Learning Coarse-to-Fine Decoding Combined Generative + Conditional Learning Speech

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Learning Structured Models for Phone Recognition Slav Petrov, Adam Pauls, Dan Klein

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Motivation (Speech) and you couldn’t care less Words: ae n d y uh k uh d n t k ae r l eh s Phones:

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Traditional Models dad Start End Begin - Middle - End Structure Triphones #-d-ad-a-da-d-# Triphones + Decision Tree Clustering d 17 =c(#-d-a)a 1 =c(d-a-d) d 9 =c(a-d-#) Mixtures of Gaussians

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Model Overview Traditional: Our Model:

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Differences to Grammars vs.

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Refinement of the ih-phone

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Inference Coarse-To-Fine Variational Approximation

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Phone Classification Results MethodError Rate GMM Baseline (Sha and Saul, 2006) 26.0 % HMM Baseline (Gunawardana et al., 2005) 25.1 % SVM (Clarkson and Moreno, 1999) 22.4 % Hidden CRF (Gunawardana et al., 2005) 21.7 % This Paper 21.4 % Large Margin GMM (Sha and Saul, 2006) 21.1 %

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Phone Recognition Results MethodError Rate State-Tied Triphone HMM (HTK) (Young and Woodland, 1994) 27.1 % Gender Dependent Triphone HMM (Lamel and Gauvain, 1993) 27.1 % This Paper 26.1 % Bayesian Triphone HMM (Ming and Smith, 1998) 25.6 % Heterogeneous classifiers (Halberstadt and Glass, 1998) 24.4 %

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Confusion Matrix

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How much supervision? Hand-aligned Exact phone boundaries are known Automatically-aligned Only sequence of phones is known

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Generative + Conditional Learning Learn structure generatively Estimate Gaussians conditionally Collaboration with Fei Sha

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Speech Synthesis Acoustic phone model: Generative Accurate Models phone internal structure well Use it for speech synthesis!

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Large Vocabulary ASR ASR System = Acoustic Model + Decoder Coarse-to-Fine Decoder: Subphone Phone Phone Syllable Word Bigram …

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Scenes Syntax Split & Merge Learning Decoding Scenes Speech

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Motivation (Scenes) Sky Water Grass Rock Seascape

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Motivation (Scenes)

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Learning Oversegment the image Extract vertical stripes Extract features Train HMMs

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Inference Decode stripes Enforce horizontal consistency

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Alternative Approach Conditional Random Fields Pro: Vertical and horizontal dependencies learnt Inference more natural Contra: Computationally more expensive

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Timeline LearningInference Syntactic MT Bayesian Conditional Summer ISI ‘07 ‘08‘09 Learning Decoding Synthesis LearningInference Syntax Scenes Speech TrecVid Now

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Results so far State of the art parser for different languages: Automatically learnt Simple & Compact Fast & Accurate Available for download Phone recognizer: Automatically learnt Competitive performance Good foundation for speech recognizer

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Proposed Deliverables Syntax Parser Speech Recognizer Speech Synthesizer Syntactic Translation Machine Scene Recognizer

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Thank You!

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Improved Inference for Unlexicalized Parsing Slav Petrov and Dan Klein.

Improved Inference for Unlexicalized Parsing Slav Petrov and Dan Klein.

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