Ph.D. Thesis Oral DefensePh.D. Thesis Oral Defense Stanford Artiﬁcial Intelligence Laboratory...

Ph.D. Thesis Oral Defense

Stanford Artificial Intelligence Laboratory (SAIL)

Valentin I. Spitkovsky

Grammar Induction and Parsingwith Dependency-and-Boundary Models

V.I. Spitkovsky (Stanford & Google) Ph.D. Thesis Oral Defense Gates #415 (2013-08-14) 1 / 60

The Problem

Problem: Unsupervised Parsing (and Grammar Induction)


The Problem


Input: Raw Text

... By most measures, the nation’s industrial sector is now

growing very slowly — if at all. Factory payrolls fell in

September. So did the Federal Reserve ...


The Problem


NN NNS VBD IN NN ♦| | | | | |

Factory payrolls fell in September .

Input: Raw Text (Sentences, Tokens and POS-tags)





The Problem




Input: Raw Text (Sentences, Tokens and POS-tags)




Output: Syntactic Structures (and a Probabilistic Grammar)


The Problem

Motivation: Unsupervised (Dependency) Parsing


The Problem


Insert your favorite reason(s) why you’d like to parseanything in the first place...


The Problem



... adjust for any data without reference tree banks:— i.e., understudied languages or genres (e.g., legal).


The Problem



... adjust for any data without reference tree banks:— i.e., understudied languages or genres (e.g., legal).

Potential applications:◮ machine translation

— word alignment, phrase extraction, reordering;

◮ web search— retrieval, query refinement;

◮ question answering, speech recognition, etc.


The Problem

Evaluation: Directed Dependency Accuracy


The Problem


Scoring example:



Directed Score: 35 = 60% (left/right-branching baselines:25 = 40%)


The Problem


Scoring example:



Directed Score: 35 = 60% (left/right-branching baselines:25 = 40%);

Undirected Score: 45 = 80% (left/right-branching baselines:45 = 80%).


Prior Art

Prior Art: A Brief History

1992 — word classes (Carroll and Charniak)1998 — greedy linkage via mutual information (Yuret)2001 — iterative re-estimation with EM (Paskin)


Prior Art



2004 — right-branching baseline— valence (DMV) (Klein and Manning)


Prior Art



2004 — right-branching baseline— valence (DMV) (Klein and Manning)

2004 — annealing techniques (Smith and Eisner)2005 — contrastive estimation (Smith and Eisner)2006 — structural biasing (Smith and Eisner)2007 — common cover link representation (Seginer)2008 — logistic normal priors (Cohen et al.)2009 — lexicalization and smoothing (Headden et al.)2009 — soft parameter tying (Cohen and Smith)


Prior Art

Prior Art: Dependency Model with Valence


Prior Art


a head-outward model, with word classesand valence/adjacency (Klein and Manning, 2004)


Prior Art



h

e.g.: verb


Prior Art



h

e.g.: verb

a1

noun


Prior Art



h

e.g.: verb

a1

nounphrase


Prior Art



h

e.g.: verb

a1

nounphrase

etc...


Prior Art



h

e.g.: verb

a1

nounphrase

etc...

a2


Prior Art



h

e.g.: verb

a1

nounphrase

etc...

a2

STOP


Prior Art



h

a1 a2

STOP

P(th) =∏

dir∈{L,R}

PSTOP(ch, dir,

adj︷︸︸︷

1n=0)

n∏

i=1

P(tai ) PATTACH(ch, dir, cai )

(1− PSTOP(ch, dir,

adj︷︸︸︷

1i=1))

n=|args(h,dir)|V.I. Spitkovsky (Stanford & Google) Ph.D. Thesis Oral Defense Gates #415 (2013-08-14) 6 / 60

Prior Art

Prior Art: Unsupervised Learning Engine


Prior Art

Prior Art: Unsupervised Learning Engineparameter fitting via expectation-maximization (EM)


Prior Art


MAGIC


Prior Art


◮ by means of inside-outside re-estimation (Baker, 1979)

w1 wmwp−1 wp wq wq+1

N1 (Manning and Schütze, 1999)

N j

· · · · · · · · ·

α

β


Prior Art


BLACK

BOX


Prior Art

Prior Art: The Standard Corpus — WSJ

5 10 15 20 25 30 35 40 45

5

10

15

20

25

30

35

40

45Sentences (1,000s)

Tokens (1,000s)100

200

300

400

500

600

700

800

900

WSJk


Prior Art

Prior Art: The Standard Corpus — WSJ10

5 10 15 20 25 30 35 40 45

5

10

15

20

25

30

35

40

45Sentences (1,000s)

Tokens (1,000s)100

200

300

400

500

600

700

800

900

WSJk


Outline

Outline

Introduction to the Grammar Induction Problem◮ The Inputs and Outputs (text → dependency parses)◮ A Probabilistic Model (DMV)◮ Parameter Fitting (EM)


Outline

Outline


Part I: Non-Convex Optimization


Outline

Outline



Part II: Parsing Constraints


Outline

Outline




Part III: Generative Models


Outline

Outline





State-of-the-Art Integrated Solution


Outline

Outline





State-of-the-Art Integrated Solution (in submission)


Outline

Optimization

Viterbi Training

Baby Steps

Lateen EM


Issues...

Issue I: Why so little data?

5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk

Directed Dependency Accuracy (%)on WSJ40

Issues...


5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Issues...


5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Oracle

Issues...

Issue II: Non-convex objectives...

maximizing the probability of data (sentence strings):

θ̂UNS = argmaxθ

∑

s

log∑

t∈T (s)

Pθ(t)

︸︷︷︸

Pθ(s)


Issues...



θ̂UNS = argmaxθ

∑

s

log∑

t∈T (s)

Pθ(t)

︸︷︷︸

Pθ(s)

supervised objective would be convex:

θ̂SUP = argmaxθ

∑

s

logPθ(t∗(s))


Issues...



θ̂UNS = argmaxθ

∑

s

log∑

t∈T (s)

Pθ(t)

︸︷︷︸

Pθ(s)

supervised objective would be convex:

θ̂SUP = argmaxθ

∑

s

logPθ(t∗(s))

could optimize likeliest parse trees (also non-convex):

θ̂VIT = argmaxθ

∑

s

maxt∈T (s)

logPθ(t)


Issues... Classic EM

Classic EM:

5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Oracle


Issues... Viterbi EM

Viterbi EM:

5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Oracle

Uninformed



Hard vs. Soft EM:

Viterbi EM: zoom in on likeliest tree



Hard vs. Soft EM:

Viterbi EM: zoom in on likeliest tree◮ does not degrade with input length◮ better retains supervised solutions◮ actually learns from scratch

— see also Cohen and Smith (2010)



Hard vs. Soft EM:



Classic EM: “focus across the board”



Hard vs. Soft EM:



Classic EM: “focus across the board”◮ hard to see the trees for the forest...

... but known to work with shorter inputs!


Baby Steps

Idea I: Baby Steps ... as Non-convex Optimization


Baby Steps


start with an easy (convex) case


Baby Steps



slowly extend it to the fully complex target task


Baby Steps




take tiny (cautious) steps in the problem space


Baby Steps





... try not to stray far from relevantneighborhoods in the solution space


Baby Steps






base case: sentences of length one (trivial — no init)


Baby Steps






base case: sentences of length one (trivial — no init)

incremental step: smooth WSJk; re-init WSJ(k + 1)


Baby Steps

Idea I: Baby Steps ... as Graduated Learning


Baby Steps


WSJ1 — Atone (verbs!)


Baby Steps



WSJ2 — It is. (pronouns!)Darkness fell.


Baby Steps



WSJ2 — It is. (pronouns!)Darkness fell.

Become a LobbyistWSJ3 — But many have. (determiners!)

They didn’t.


Baby Steps

Idea I: Baby Steps ... and Related Notions


Baby Steps


shaping (Skinner, 1938)


Baby Steps


shaping Psychology / Cognitive Science (Skinner, 1938)

less is more (Kail, 1984; Newport, 1988; 1990)

starting small (Elman, 1993)

◮ scaffold on model complexity [restrict memory]◮ scaffold on data complexity [restrict input]


Baby Steps






stepping stones (Brown et al., 1993)

coarse-to-fine NLP / AI (Charniak and Johnson, 2005)

curriculum learning (Bengio et al., 2009)


Baby Steps









continuation methods (Allgower and Georg, 1990)

deterministic annealing Math / OR (Rose, 1998)


Baby Steps









continuation methods (Allgower and Georg, 1990)

deterministic annealing Math / OR (Rose, 1998)

successive approximations!


Baby Steps

Aside I: Speaking of successive approximations...


Baby Steps

Idea I: Baby Steps ... Observations & Results!

5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Oracle

Baby Steps


5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Oracle

Baby Steps

Baby Steps


5 10 15 20 25 30 35 40

10

20

30

40

50

60

70

WSJk


Uninformed

Oracle

Baby Steps

Less is More︸︷︷︸

K&M∗


Leapfrog

Idea II: Less is More & Leapfrog ... a Hack!

5 10 15 20 25 30 35 40

20

30

40

50

60

70

80

90

WSJk

Directed Dependency Accuracy (%)on WSJk

Oracle

Uninformed

Baby StepsK&M

Leapfrog


5 10 15 20 25 30 35 40

20

30

40

50

60

70

80

90

WSJk


Oracle

Uninformed

Baby Steps

K&M∗

Leapfrog


5 10 15 20 25 30 35 40

20

30

40

50

60

70

80

90

WSJk


Oracle

Uninformed

Baby Steps

K&M∗

Leapfrog


Leapfrog

Aside II: Also, to be fair...

5 10 15 20 25 30 35 40

20

30

40

50

60

70

80

90

WSJk

Undirected Dependency Accuracy (%)on WSJk

Uninformed


Lateen EM

Lateen EM: Intuition

Use both objectives (a primary and a secondary).


Lateen EM



As a captain can’t count on favorable winds, so anunsupervised learner can’t rely on co-operative gradients.


Lateen EM



As a captain can’t count on favorable winds, so anunsupervised learner can’t rely on co-operative gradients.

Lateen strategies de-emphasize fixed points, e.g., bytacking around local attractors, in a zig-zag fashion.


Lateen EM

Lateen EM: Simple Algorithm


Lateen EM


Alternate ordinary soft and hard EM algorithms:switching when stuck helps escape local optima.


Lateen EM


Alternate ordinary soft and hard EM algorithms:switching when stuck helps escape local optima.

E.g., Italian grammar induction improvesfrom 41.8% to 56.2% after three lateen alternations:

50 100 150 200 250 300

3.03.54.04.5

3.39

3.26

3.42

3.19

3.33

3.23

3.39

3.18

3.29

3.21

3.39

3.18

3.29

3.22

bpt

iteration

Pumping action: – hard EM pushes down the top curve (primary objective);– soft EM pushes down the bottom curve (the secondary

objective), often at the expense of the primary.


Lateen EM

Lateen EM: Early-Stopping


Lateen EM


Use one objective to validate moves proposed by theother: stop if the secondary objective gets worse.


Lateen EM


Use one objective to validate moves proposed by theother: stop if the secondary objective gets worse.

30% faster, on average, than either standard EM.


Lateen EM

Optimization: Summary


Lateen EM


Take guesswork out of tuning EM:


Lateen EM


Take guesswork out of tuning EM:◮ Lateen EM ties termination to a sign change


Lateen EM


Take guesswork out of tuning EM:◮ Lateen EM ties termination to a sign change;◮ Viterbi EM and Baby Steps don’t require initializers.


Lateen EM



Exploit multiple views and iterate (Blum and Mitchell, 1998):


Lateen EM




◮ Lateen EM optimizes for Viterbi parse trees and forests;


Lateen EM




◮ Lateen EM optimizes for Viterbi parse trees and forests;◮ Baby Steps focuses on simpler sentences in the data.


Lateen EM





Capitalize on EM’s advantages:


Lateen EM





Capitalize on EM’s advantages:◮ guarantee to not harm a primary likelihood


Lateen EM





Capitalize on EM’s advantages:◮ guarantee to not harm a primary likelihood;◮ begin with large steps in a parameter space.


Lateen EM






Ameliorate EM’s disadvantages:


Lateen EM






Ameliorate EM’s disadvantages:◮ avoid taking disproportionately many (and ever-smaller)

steps to approach a likelihood’s fixed point


Lateen EM






Ameliorate EM’s disadvantages:◮ avoid taking disproportionately many (and ever-smaller)

steps to approach a likelihood’s fixed point;◮ escape by changing objectives and mixing solutions!


Lateen EM

Constraints

Web Markup

Punctuation

Capitalization


Overview

Constraints: Supervised and Unsupervised


Overview

Constraints: Supervised and Unsupervisedcompact summaries of high-level insights into a domain


Overview

Constraints: Supervised and Unsupervisedcompact summaries of high-level insights into a domain— can significantly reduce the search space


Overview

Constraints: Supervised and Unsupervisedcompact summaries of high-level insights into a domain— can significantly reduce the search space— often easier to enforce than to model


Overview


relevant to unsupervised learning (less rope to hang self)


Overview


relevant to unsupervised learning (less rope to hang self)— in general, steer at the “right” regularities in data


Overview


relevant to unsupervised learning (less rope to hang self)— in general, steer at the “right” regularities in data— linguistic structure underdetermined by raw text


Overview



partial bracketings (Pereira and Schabes, 1992)


Overview

Constraints: Supervised and Unsupervised

compact summaries of high-level insights into a domain— can significantly reduce the search space— often easier to enforce than to model


partial bracketings (Pereira and Schabes, 1992)

synchronous grammars (Alshawi and Douglas, 2000)

linear-time parsing, skewness of trees, etc. (Seginer, 2007)

sparse posterior regularization (Ganchev et al., 2009)


Linguistic Analysis

Syntax of Markup: Common Constituents

..., but [S [NP the Toronto Star][VP reports [NP this][PP in the softest possible way],[S stating ...]]]


Linguistic Analysis



S → NP VP


Linguistic Analysis



S → NP VP→ DT NNP NNP VBZ NP PP S


Linguistic Analysis

Syntax of Markup: Constituent Productions

%NP → NNP NNP 9.6NP → NNP 4.6NP → NP PP 3.4

NP → NNP NNP NNP 2.4NP → DT NNP NNP 2.1NP → NN 1.8

NP → DT NNP NNP NNP 1.7NP → DT NN 1.7NP → DT NNP NNP 1.6S → NP VP 1.4

NP → DT NNP NNP NNP 1.2NP → DT JJ NN 1.1NP → NNS 1.0NP → JJ NN 0.8NP → NP NP 0.8

35.3


Linguistic Analysis

Syntax of Markup: Common Dependencies


DT NNP NNP VBZ DT IN DT JJS JJ NN


Linguistic Analysis




DT NNP VBZ


Linguistic Analysis




DT NNP VBZ

“the Star reports”


Linguistic Analysis

Syntax of Markup: Head-Outward Spawns

%NNP 24.4NN 8.1

DT NNP 6.1DT NN 5.9NNS 4.5NNPS 1.4VBG 1.3

NNP NNP NN 1.2VBD 1.0IN 1.0VBN 1.0

DT JJ NN 0.9VBZ 0.9

POS NNP 0.9JJ 0.8

59.4


Transition Formatting

Formatting:

strong connection between markup and syntax



Formatting:


yields a suite of accurate parsing constraints



Formatting:



improves grammar induction over web data



Formatting:



improves grammar induction over web data, but...



Formatting:



improves grammar induction over web data, but...◮ works better with more natural language-resources!



Formatting:




missing structural cues:



Formatting:




missing structural cues:— e.g., punctuation (will make heavy use)



Formatting:





and capitalization (will not discuss today)



Formatting:





and capitalization (will not discuss today)

raw word streams often difficult even for humans— e.g., transcribed utterances (Kim and Woodland, 2002)


Example Raw Text

Example: Raw Word Stream

ALTHOUGH IT PROBABLY HAS REDUCED THELEVEL OF EXPENDITURES FOR SOME

PURCHASERS UTILIZATION MANAGEMENTLIKE MOST OTHER COST CONTAINMENTSTRATEGIES DOESN’T APPEAR TO HAVE

ALTERED THE LONG-TERM RATE OFINCREASE IN HEALTH-CARE COSTS THE

INSTITUTE OF MEDICINE AN AFFILIATE OFTHE NATIONAL ACADEMY OF SCIENCESCONCLUDED AFTER A TWO-YEAR STUDY


Example Formatted Text

Example:



Example:

[SBAR Although it probably has reduced the level ofexpenditures for some purchasers],



Example:

[SBAR Although it probably has reduced the level ofexpenditures for some purchasers], [NP utilization

management] —



Example:


management] — [PP like most other costcontainment strategies] —



Example:


management] — [PP like most other costcontainment strategies] — [VP doesn’t appear to

have altered the long-term rate of increase inhealth-care costs],



Example:



have altered the long-term rate of increase inhealth-care costs], [NP the Institute of Medicine],



Example:




[NP an affiliate of the National Academy of

Sciences],



Example:




[NP an affiliate of the National Academy of

Sciences], [VP concluded after a two-year study].


Example Strong Assumption

Intuition:

strong constraint



Intuition:

strong constraint: (head ← head) in training



Intuition:


word head , head word word ,

head word word word word word word word .



Intuition:


Other countries , including West Germany ,

may have a hard time justifying continued membership .


Example Weak Assumption

Intuition:

weak constraint



Intuition:

weak constraint: (head ← external word) in inference



Intuition:


word word head word word word ,

head word word word word word word word .



Intuition:


IFI also has nonvoting preferred shares ,

which are quoted on the Milan stock exchange .


Linguistic Analysis Constituents

Linguistic Analysis:

punctuation and syntax are related(Nunberg, 1990; Briscoe, 1994;Jones 1994; Doran, 1998, inter alia)





49.4% of inter-punctuationfragments are constituents





49.4% of inter-punctuationfragments are constituents

many fragments deriveprecisely themselves:

%IN 9.6NN 7.2NNP 6.3CD 3.8VBD 3.2VBZ 3.0RB 2.8VBG 2.2VBP 1.9NNS 1.8WDT 1.6MD 1.1VBN 1.1

IN VBD 1.0JJ 0.7

52.8


Linguistic Analysis Strong Dependencies

Training:

strong constraint, e.g.,

... arrests followed a “ Snake Day ” at Utrecht ...

— already 74.0% agreement with head-percolated trees.


Linguistic Analysis Weak Dependencies

Inference:

weak constraint, e.g.,

Maryland Club also distributes tea , which ...

— now 92.9% agreement with head-percolated trees!


Linguistic Analysis Weak Dependencies

Modeling

Context-Sensitive Unsupervsed Tags

Dependency-and-Boundary Models

Reduced Models of Grammar


Gold Tags Unlexicalized Tokens

Example: Actually, state-of-the-art uses gold tags!

IN PRP RB VBZ VBN DT NN IN NNS IN DTNNS NN NN IN RBS JJ NN NN NNS VBZ RBVB TO VB VBN DT JJ NN IN NN IN JJ NNSDT NNP IN NNP DT NN IN DT NNP NNP IN

NNPS VBD IN DT JJ NN



Word Categories: Two benefits of tag-sets...Grouping: pooling statistics of words that play similarsyntactic roles improves generalization (reduces sparsity):




◮ indeed, working directly with words does not do well.





Disambiguation: for words that take on multiple parts ofspeech, knowing gold tags limits the parsing search space:






◮ reducing manual tags to monosemous clusterings lowersperformance below that of the unsupervised categoriesconstructed by Finkel and Manning (2009) / Clark (2003).







Can improve with context-sensitive unsupervised clusters!








1 start with a hard assignment; (standard word clustering)








1 start with a hard assignment; (standard word clustering)2 inject context-colored noise; (get out of the local optimum)








1 start with a hard assignment; (standard word clustering)2 inject context-colored noise; (get out of the local optimum)3 Viterbi-train a bitag HMM. (the unTagger)



Word Categories: Breaking the barrier...

Swapped out gold tags for unsupervised word categories:

System Description Accuracy

“punctuation” with gold tags 58.4






“punctuation” with gold tags 58.4“punctuation” with monosemous induced tags 58.2 (−0.2)






“punctuation” with gold tags 58.4“punctuation” with monosemous induced tags 58.2 (−0.2)“punctuation” with context-sensitive induced tags 59.1 (+0.7)







◮ only a small drop from switching to (monosemous)unsupervised clusters — previous systems lost ∼ 5pts







◮ only a small drop from switching to (monosemous)unsupervised clusters — previous systems lost ∼ 5pts;

◮ first state-of-the-art system to improve with(context-sensitive) unsupervised clusters!



Dependency-and-Boundary Models (DBMs):

Use boundary cues in head-driven dependency grammars.





E.g., induce structure by working inwards from edges





E.g., induce structure by working inwards from edges:

DT NN VBZ IN DT NN

| | | | | |

[The check] is in [the mail].︸︷︷︸

Subject NP︸︷︷︸

Object NP






DT NN VBZ IN DT NN

| | | | | |



Object NP

◮ learn from left fringe (determiner DT) to parse object NP






DT NN VBZ IN DT NN

| | | | | |



Object NP

◮ learn from left fringe (determiner DT) to parse object NP◮ based on right fringe (noun NN), correctly parse subject NP






DT NN VBZ IN DT NN

| | | | | |



Object NP

◮ learn from left fringe (determiner DT) to parse object NP◮ based on right fringe (noun NN), correctly parse subject NP◮ between them, glean make-up of larger phrases (e.g., VP)



Dependency-and-Boundary Models: Highlights




DBM-1: Use words at the fringes.





◮ truly head-outward model (Alshawi, 1996)





◮ truly head-outward model (Alshawi, 1996)◮ conditions on what can more often be seen!






DBM-2: Fragments differ from complete sentences.







◮ incomplete fragments are uncharacteristically short







◮ incomplete fragments are uncharacteristically short◮ roots of fragments are generally not verbs or modals







◮ incomplete fragments are uncharacteristically short◮ roots of fragments are generally not verbs or modals

DBM-3: Learn to stitch together fragments.



Class-based, head-outward generation (Alshawi, 1996)




chce

dir = R adj = T




ch

dir = R adj = T

cd1

ce




ch

dir = R

cd1

ceadj = F




ch

dir = R

cd1

adj = F

cd2

ce




ch

dir = R

cd1

adj = F

cd2

ceSTOP




ch

dir = R

cd1

adj = F

cd2

ceSTOP

PROOT(ch | comp)




ch

dir = R

cd1

adj = F

cd2

ceSTOP

PROOT(ch | comp) PATTACH(cd | ch, dir , cross)




ch

dir = R

cd1

adj = F

cd2

ceSTOP

PROOT(ch | comp) PATTACH(cd | ch, dir , cross) PSTOP(| dir , adj , ce, comp)


Reduced Models

How to better exploit more data better?

more text in long sentences, but those can be hard...


Reduced Models


more text in long sentences, but those can be hard...◮ require Viterbi training, punctuation constraints, etc.


Reduced Models



could we “start small” and still use more data?


Reduced Models



could we “start small” and still use more data?◮ ... and wouldn’t it be nice if we could just split things up!


Reduced Models What If

What if we chopped up input at punctuation?

impact on quantity of data (with a 15-token threshold):◮ more and simpler word sequences incorporated earlier




impact on quantity of data (with a 15-token threshold):◮ more and simpler word sequences incorporated earlier◮ much more dense coverage of available data:

⋆ number of training inputs goes up 2.2x⋆ number of tokens increases 4.3x






but, also impact on quality of data






but, also impact on quality of data:◮ many fewer complete sentences exhibiting full structure






but, also impact on quality of data:◮ many fewer complete sentences exhibiting full structure◮ even less representative than short inputs:

⋆ but mostly phrases and clauses...






but, also impact on quality of data:◮ many fewer complete sentences exhibiting full structure◮ even less representative than short inputs:

⋆ but mostly phrases and clauses...

... however, we have an appropriate model family!


Reduced Models Example Continued

Example (cont’d)



Example (cont’d)

length & type left & right

complete 51 S IN NN

incomplete 12 SBAR IN NNS2 NP NN NN6 PP IN NNS

14 VP VBZ NNS4 NP DT NNP

8 NP DT NNPS5 VP VBD NN



Example (cont’d)

DBM-2length & type left & right

complete 51 S IN NN






Example (cont’d)

DBM-1length & type left & right

complete 51 S IN NN






Example (cont’d)


complete 51 S IN NN



8 NP DT NNPSDBM-3 5 VP VBD NN

partial parse forests “easy-first” (Goldberg and Elhadad, 2010)



Example (cont’d)


complete 51 S IN NN


reduced model 14 VP VBZ NNSDBM-0 4 NP DT NNP


partial parse forests “easy-first” (Goldberg and Elhadad, 2010)



Integrated System



System: Combiners... (from Leapfrog)



LD

LD

LD

+arg

MAXLD

C1C ∗1 = L(C1)

C2C ∗2 = L(C2)

C ∗1 + C∗2 = C+



LD

LD

LD

+arg

MAXLD

C1C ∗1 = L(C1)

C2C ∗2 = L(C2)

C ∗1 + C∗2 = C+

LDC2

C1



System: Inductors... (from Baby Steps and Lateen EM)



HL·DBMD

SDBMD

SDBM0D

C

F

S

D0

C1

C2

C ′1

C ′2



HL·DBMD

SDBMD

SDBM0D

C

F

S

D0

C1

C2

C ′1

C ′2

CD

lsplit

Dl+1split



System: Iterating... (from Baby Steps, Less is More, etc.)



1 2 14 15



1 2 14 15

HL·DBMDl+1

split∅Cl Cl+1l+1

l+1



1 2 14 15

HL·DBMDl+1

split∅Cl Cl+1l+1

l+1

HL·DBMD45split

HL·DBMD45C



System: Results ... on Section 23 of WSJ




Right-Branching (Klein and Manning, 2004) 31.7%




Right-Branching (Klein and Manning, 2004) 31.7%DMV @10 34.2%




Right-Branching (Klein and Manning, 2004) 31.7%DMV @10 34.2%Baby Steps @15 39.2%Baby Steps @45 39.4%




Right-Branching (Klein and Manning, 2004) 31.7%DMV @10 34.2%Baby Steps @15 39.2%Baby Steps @45 39.4%Soft Parameter Tying (Cohen and Smith, 2009) 42.2%




Right-Branching (Klein and Manning, 2004) 31.7%DMV @10 34.2%Baby Steps @15 39.2%Baby Steps @45 39.4%Soft Parameter Tying (Cohen and Smith, 2009) 42.2%Less is More @15 44.1%




Right-Branching (Klein and Manning, 2004) 31.7%DMV @10 34.2%Baby Steps @15 39.2%Baby Steps @45 39.4%Soft Parameter Tying (Cohen and Smith, 2009) 42.2%Less is More @15 44.1%Leapfrog @45 45.0%





(Gimpel and Smith, 2012) 53.1%(Gillenwater et al., 2010) 53.3%

(Bisk and Hockenmaier, 2012) 53.3%(Blunsom and Cohn, 2010) 55.7%

(Tu and Honavar, 2012) 57.0%





(Gimpel and Smith, 2012) 53.1%(Gillenwater et al., 2010) 53.3%

(Bisk and Hockenmaier, 2012) 53.3%(Blunsom and Cohn, 2010) 55.7%

(Tu and Honavar, 2012) 57.0%Integrated System — no initializers or gold tags — 64.4%

V.I. Spitkovsky (Stanford & Google) Ph.D. Thesis Oral

Ph.D. Thesis Oral DefensePh.D. Thesis Oral Defense Stanford Artiﬁcial Intelligence Laboratory...

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