structured prediction: a large margin approach ben taskar university of pennsylvania joint work...

Structured Prediction:A Large Margin Approach

Ben TaskarUniversity of Pennsylvania

Joint work with:

V. Chatalbashev, M. Collins, C. Guestrin, M. Jordan, D. Klein, D. Koller, S. Lacoste-Julien, C. Manning

“Don’t worry, Howard. The big questions are multiple choice.”

Handwriting Recognition

brace

Sequential structure

x y

Object Segmentation

Spatial structure

x y

Natural Language Parsing

The screen was a sea of red

Recursive structure

x y

Bilingual Word Alignment

What is the anticipated cost of collecting fees under the new proposal?

En vertu des nouvelles propositions, quel est le coût prévu de perception des droits?

x yWhat

is the

anticipated

costof

collecting fees

under the

new proposal

?

En vertu delesnouvelles propositions, quel est le coût prévu de perception de les droits?Combinatorial structure

Protein Structure and Disulfide Bridges

Protein: 1IMT

AVITGACERDLQCGKGTCCAVSLWIKSVRVCTPVGTSGEDCHPASHKIPFSGQRMHHTCPCAPNLACVQTSPKKFKCLSK

Local Prediction

Classify using local information Ignores correlations & constraints!

br ea c

Local Predictionbuildingtreeshrubground

Structured Prediction

Use local information Exploit correlations

br ea c

Structured Predictionbuildingtreeshrubground

Outline Structured prediction models

Sequences (CRFs) Trees (CFGs) Associative Markov networks (Special MRFs) Matchings

Structured large margin estimation Margins and structure Min-max formulation Linear programming inference Certificate formulation

Structured Models

Mild assumption:

linear combination

space of feasible outputs

scoring function

Chain Markov Net (aka CRF*)

a-z

a-z

a-z

a-z

a-z

y

x

*Lafferty et al. 01

Associative Markov Nets

Point featuresspin-images, point height

Edge featureslength of edge, edge orientation

yj

yk

jk

j

“associative” restriction

CFG Parsing

#(NP DT NN)

…

#(PP IN NP)

…

#(NN ‘sea’)

Bilingual Word Alignment

position orthography association

Whatis

theanticipate

dcost

ofcollecting

fees under

the new

proposal?

En vertu delesnouvelles propositions, quel est le coût prévu de perception de le droits?

j

k

Disulfide Bonds: Non-bipartite Matching

1

2 3

4

6 5

RSCCPCYWGGCPWGQNCYPEGCSGPKV 1 2 3 4 5 6

6

1

2

4

5

3

Fariselli & Casadio `01, Baldi et al. ‘04

Scoring Function



1

2 3

4

6 5

amino acid identities phys/chem properties

Structured Models

Mild assumptions:

linear combination

sum of part scores

space of feasible outputs

scoring function

Supervised Structured Prediction

Learning Prediction

Estimate w

Example:Weighted matching

Generally: Combinatorial

optimization

Data

Model:

Likelihood(intractable)

MarginLocal(ignores

structure)

We want:

Equivalently:

OCR Example

a lot!…

“brace”

“brace”

“aaaaa”

“brace” “aaaab”

“brace” “zzzzz”

We want:

Equivalently:

‘It was red’

Parsing Example

a lot!

SA B

C D

SA BD F

SA B

C D

SE F

G H

SA B

C D

SA B

C D

SA B

C D

…

‘It was red’

‘It was red’

‘It was red’

‘It was red’

‘It was red’

‘It was red’

We want:

Equivalently:

‘What is the’‘Quel est le’

Alignment Example

a lot!…

123

123


123

123


123

123


123

123

123

123

123

123

123

123




Structured Loss

b c a r e b r o r e b r o c eb r a c e

2 2 10

123

123

123

123

123

123

123

123


0 1 2 2S

A EC D

SB E

A C

SB D

A C

SA B

C D‘It was red’

0 1 2 3

Large margin estimation Given training examples , we want:

Maximize margin

Mistake weighted margin:

# of mistakes in y

*Collins 02, Altun et al 03, Taskar 03

Large margin estimation

Eliminate

Add slacks for inseparable case

Large margin estimation Brute force enumeration

Min-max formulation

‘Plug-in’ linear program for inference

Min-max formulation

LP Inference

Structured loss (Hamming):

Inference

discrete optim.

Key step:

continuous optim.

y z Map for Markov Nets

0 1 . 0

0 0 . 0

. . . 0

0 0 0 0

1

0

:

0

0

1

:

0

1

0

:

0

0

1

:

0

0

1

:

0

a

b

:

z

0 0 . 0

1 0 . 0

. . . 0

0 0 0 0

0 1 . 0

0 0 . 0

. . . 0

0 0 0 0

0 0 . 0

0 1 . 0

. . . 0

0 0 0 0

a

b

:

z

a b . z a b . z a b . z a b . z

Markov Net Inference LP

0 0 0 0

0 0 0 0

0 1 0 0

0 0 0 0

0

0

1

0

0 1 0 0

Has integral solutions z for chains, treesCan be fractional for untriangulated networks

normalization

agreement

Associative MN Inference LP

For K=2, solutions are always integral (optimal) For K>2, within factor of 2 of optimal

“associative” restriction

0

1

0

0

0

1

0

0

0 1 0 0

CFG Chart

CNF tree = set of two types of parts: Constituents (A, s, e) CF-rules (A B C, s, m, e)

CFG Inference LP

inside

outside

Has integral solutions z

root

Matching Inference LP

Has integral solutions z

degree

Whatis

theanticipate

dcost

ofcollecting

fees under

the new

proposal?

En vertu delesnouvelles propositions, quel est le coût prévu de perception de le droits?

j

k

LP Duality Linear programming duality

Variables constraints Constraints variables

Optimal values are the same When both feasible regions are bounded

Min-max Formulation

LP duality

Min-max formulation summary

Formulation produces concise QP for Low-treewidth Markov networks Associative MNs (K=2) Context free grammars Bipartite matchings Approximate for untriangulated MNs, AMNs with K>2

*Taskar et al 04

Unfactored Primal/Dual

QP duality

Exponentially many constraints/variables

Factored Primal/Dual

By QP duality

Dual inherits structure from problem-specific inference LP

Variables correspond to a decomposition of variables of the flat case

The Connection

b c a r e b r o r e b r o c eb r a c e

rc

ao

cr

.2.15.25

.4

.2 .35

.65.8

.4

.61b 1e

2 2 10

Duals and Kernels

Kernel trick works: Factored dual Local functions (log-potentials) can use

kernels

Simple iterative method

Unstable for structured output: fewer instances, big updates

May not converge if non-separable Noisy

Voted / averaged perceptron [Freund & Schapire 99, Collins 02]

Regularize / reduce variance by aggregating over iterations

Alternatives: Perceptron

Add most violated constraint

Handles more general loss functions Only polynomial # of constraints needed Need to re-solve QP many times Worst case # of constraints larger than

factored

Alternatives: Constraint Generation

[Collins 02; Altun et al, 03; Tsochantaridis et al, 04]

Handwriting Recognition

Length: ~8 charsLetter: 16x8 pixels 10-fold Train/Test5000/50000

letters600/6000 words

Models: Multiclass-SVMs* CRFs M3 nets

*Crammer & Singer 01

0

5

10

15

20

25

30

CRFsMC–SVMs M^3 nets

Te

st e

rro

r (a

vera

ge

pe

r-c

ha

ract

er) raw

pixelsquadratic

kernelcubickernel

45% error reduction over linear CRFs33% error reduction over multiclass

SVMs

better

0

5

10

15

20

Tes

t Err

or

SVMs RMNS M^3Ns

Hypertext Classification WebKB dataset

Four CS department websites: 1300 pages/3500 links Classify each page: faculty, course, student, project, other Train on three universities/test on fourth

53% error reduction over SVMs

38% error reduction over RMNs

relaxed dual

*Taskar et al 02

better

loopy belief propagation

3D Mapping

Laser Range Finder

GPS

IMU

Data provided by: Michael Montemerlo & Sebastian Thrun

Label: ground, building, tree, shrub Training: 30 thousand points Testing: 3 million points

Segmentation results

Hand labeled 180K test pointsModel

Accuracy

SVM 68%

V-SVM

73%

M3N 93%

Fly-through

Word Alignment Results

Model *Error

Local learning+matching 10.0

Our approach 8.5

Data: [Hansards – Canadian Parliament] Features induced on 1 mil unsupervised sentences Trained on 100 sentences (10,000 edges) Tested on 350 sentences (35,000 edges)

[Taskar+al 05]

*Error: weighted combination of precision/recall [Lacoste-Julien+Taskar+al 06]

GIZA/IBM4 [Och & Ney 03] 6.5

+Our approach+QAP 4.5

+Local learning+matching 5.4

+Our approach 4.9

Certificate formulation Non-bipartite matchings:

O(n3) combinatorial algorithm No polynomial-size LP known

Spanning trees No polynomial-size LP known Simple certificate of optimality

Intuition: Verifying optimality easier than optimizing

Compact optimality condition of wrt.

1

2 3

4

6 5

ijkl

Certificate for non-bipartite matching

Alternating cycle: Every other edge is in matching

Augmenting alternating cycle: Score of edges not in matching greater than edges in matching

Negate score of edges not in matching Augmenting alternating cycle = negative length alternating

cycle

Matching is optimal no negative alternating cycles

1

2 3

4

6 5

Edmonds ‘65

Certificate for non-bipartite matching

Pick any node r as root

= length of shortest alternating

path from r to j

Triangle inequality:

Theorem:

No negative length cycle distance function d exists

Can be expressed as linear constraints: O(n) distance variables, O(n2) constraints

1

2 3

4

6 5

Certificate formulation

Formulation produces compact QP for Spanning trees Non-bipartite matchings Any problem with compact optimality condition

*Taskar et al. ‘05

Disulfide Bonding Prediction Data [Swiss Prot 39]

450 sequences (4-10 cysteines) Features:

windows around C-C pair physical/chemical properties

[Taskar+al 05]

Model *Acc

Local learning+matching 41%

Recursive Neural Net [Baldi+al’04] 52%

Our approach (certificate) 55%

*Accuracy: % proteins with all correct bonds

Formulation summary

Brute force enumeration

Min-max formulation ‘Plug-in’ convex program for inference

Certificate formulation Directly guarantee optimality of

Omissions Kernels

Non-parametric models

Structured generalization bounds Bounds on hamming loss

Scalable algorithms (no QP solver needed) Structured SMO (works for chains, trees)

[Taskar 04] Structured ExpGrad (works for chains, trees)

[Bartlett+al 04] Structured ExtraGrad (works for matchings, AMNs)

[Taskar+al 06]

Open questions Statistical consistency

Hinge loss not consistent for non-binary output [See Tewari & Bartlett 05, McAllester 07]

Learning with approximate inference Does constant factor approximate inference

guarantee anything about learning? No [See Kulesza & Pereira 07] Perhaps other assumptions needed

Discriminative structure learning Using sparsifying priors

Conclusion Two general techniques for structured large-margin

estimation

Exact, compact, convex formulations

Allow efficient use of kernels

Tractable when other estimation methods are not

Efficient learning algorithms

Empirical success on many domains

ReferencesY. Altun, I. Tsochantaridis, and T. Hofmann. Hidden

Markov support vector machines. ICML03.M. Collins. Discriminative training methods for hidden

Markov models: Theory and experiments with perceptron algorithms. EMNLP02

K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. JMLR01

J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML04

More papers at http://www.cis.upenn.edu/~taskar

Modeling First Order Effects

Monotonicity Local inversion Local fertility

QAP NP-complete Sentences (30 words, 1k vars) few seconds (Mosek) Learning: use LP relaxation Testing: using LP, 83.5% sentences, 99.85% edges integral

Segmentation Model Min-Cut

0 1

Local evidence

Spatial smoothness

Computing is hard in general, but if edge potentials attractive min-cut algorithmMultiway-cut for multiclass case use LP relaxation

[Greig+al 89, Boykov+al 99, Kolmogorov & Zabih 02, Taskar+al 04]

Scalable Algorithms Batch and online Linear in the size of the data

Iterate until convergence For each example in the training sample

Run inference using current parameters (varies by method) Online: Update parameters using computed example values

Batch: Update parameters using computed sample values

Structured SMO (Taskar et al, 03; Taskar 04) Structured Exponentiated Gradient (Bartlett et al, 04)Structured Extragradient (Taskar et al, 05)

Experimental Setup Standard Penn treebank split (2-21/22/23) Generative baselines

Klein & Manning 03 and Collins 99 Discriminative

Basic = max-margin version of K&M 03 Lexical & Lexical + Aux

Lexical features (on constituent parts only)ts-1 [ts … te] te+1 predicted tags

xs-1 [xs … xe] xe+1

Auxillary features Flat classifier using same features Prediction of K&M 03 on each span

Results for sentences ≤40 words

Model LP LR F1

Generative 86.37 85.27 85.82

Lexical+Aux* 87.56 86.85 87.20

Collins 99* 85.33 85.94 85.73

*Trained only on sentences ≤20 words

*Taskar et al 04

Example

The Egyptian president said he would visit Libya today to resume the talks.

Generative model: Libya today is base NP

Lexical model: today is a one word constituent

structured prediction: a large margin approach ben taskar university of pennsylvania joint work...

Documents

function slide

manning slide

combinatorial structure

nn sea slide

protein structure

cot prvu

nouvelles propositions

anticipated cost