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Hitting The Right Paraphrases In Good Time

Stanley KokDept. of Comp. Sci. & Eng.

Univ. of WashingtonSeattle, USA

Chris BrockettNLP Group

Microsoft ResearchRedmond, USA

Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

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What’s a paraphrase of…

ParaphraseSystem

“is on good terms with”

• “is friendly with”

• “is a friend of”• …

Query expansion Document summarization Natural language generation Question answering etc.

Applications

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What’s a paraphrase of…

ParaphraseSystem

“is on good terms with”

• “is friendly with”

• “is a friend of”• …

Bilingual Parallel Corpora

English Phrase (E)

German Phrase (G)

P(G|E) P(E|G)

under control unter kontrolle 0.75 0.40

in check unter kontrolle 0.60 0.20

... … … …6

Bilingual Parallel Corpus

…the cost dynamic is under control……die kostenentwicklung unter kontrolle……keep the cost in check……die kosten unter kontrolle………

Phrase Table

BCB system [Bannard & Callison-Burch, ACL’05]

P(E2|E1) ¼C G P(E2|G) P(G|E1)

SBP system [Callison-Burch, EMNLP’08]

P(E2|E1) ¼C G P(E2|G,syn(E1)) p(G|E1, syn(E1))

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State of the Art

8E1E2

G1 F2

P(F2|E1)

P(E2|F2)

P(G1|E1)P(E2|G1)

E3E4

(in check) (under control)

G2G3

(unter kontrolle)F1

Graphical View

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Graphical ViewPath lengths > 2General graphAdd nodes to represent domain knowledge

Random WalksHitting Times

G1 F2G2G3 F1

E1E2E3E4

Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

AA

Random Walk Begin at node A Randomly pick neighbor n

E

F

D

B

C11

Random Walk Begin at node A Randomly pick neighbor n Move to node n

E

F

D A

2B

C12

Random Walk Begin at node A Randomly pick neighbor n Move to node n Repeat

E

F

D A

B

2C13

Expected number of steps starting from node i before node j is visited for first time Smaller hitting time → closer to start node i

Truncated Hitting Time [Sarkar & Moore, UAI’07]

Random walks are limited to T steps Computed efficiently & with high probability by

sampling random walks [Sarkar, Moore & Prakash ICML’08]

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Hitting Time from node i to j

Finding Truncated Hitting Time By Sampling

E

F

D 1

B

C

A

A

T=5

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Finding Truncated Hitting Time By Sampling

E

F

4 A

B

C

D

A D

T=5

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Finding Truncated Hitting Time By Sampling

5

F

D A

B

C

E

A D E

T=5

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Finding Truncated Hitting Time By Sampling

E

F

4 A

B

C

D

A D E D

T=5

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Finding Truncated Hitting Time By Sampling

E

6

D A

B

CF

A D E D F

T=5

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Finding Truncated Hitting Time By Sampling

5

F

D A

B

C

E

A D E D F E

T=5

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Finding Truncated Hitting Time By Sampling

A D E D F E

T=5

E

F

D A

B

C

hAD=1hAE=2

hAF=4

hAA=0hAB=5

hAC=5

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Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

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Hitting Time Paraphraser (HTP)

ParaphraseSystem

“is on good terms with”

• “is friendly with”

• “is a friend of”• …

HTP

Phrase TablesEnglish-GermanEnglish-FrenchGerman-Frenchetc.

Phrase Paraphrases

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Graph Construction

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Graph Construction

BFS from query phrase up to depth d or up to max. number n of nodes d = 6, n = 50,000

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… … … ……

…

……

…Graph Construction

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Graph Construction

… … … ……

…

……

…

0.250.35

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Graph Construction

… … … ……

…

……

…

0.6

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Graph Construction

… … … ……

…

……

…

0.50.5

Run m truncated random walks to estimate truncated hitting time of each node T = 10, m = 1,000,000

Prune nodes with hitting times = T

Estimate Trunc. Hitting Times

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Add Ngram Nodes

“achieve the goal”“achieve the aim”“reach the objective”

“the”……

“achieve the” “the aim”“reach” “objective”

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Add “Syntax” Nodes

“whose goal is” “the aim is”“the objective is” “what goal”

start with article end with be start with interrogatives

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Add Not-Substring-Of Nodes

“reach the” “reach the aim”“reach the objective” “objective”

not-substring-of

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Feature Nodes

ngram nodes

“syntax” nodes

not-substring nodes

phrase nodes

p2

p1

p3

p4 = 0.4= 0.1

= 0.4

= 0.1

Run m truncated random walks again Rank paraphrases in increasing order of

hitting times

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Re-estimate Truncated Hitting Times

Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

Europarl dataset [Koehn, MT-Summit’05]

Use 6 of 11 languages: English, Danish, German, Spanish, Finnish, Dutch

About a million sentences per language English−Foreign phrasal alignments by giza++

[Callison-Burch, EMNLP’08]

Foreign−Foreign phrasal alignments by MSR aligner

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Data

SBP system [Callison-Burch, EMNLP’08]

HTP with no feature node HTP with bipartite graph

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Comparison Systems

NIST dataset 4 English translations per Chinese sentence 33,216 English translations

Randomly selected 100 English phrases From 1-4grams in both NIST & Europarl datasets Exclude stop words, numbers, phrases containing

periods and commas

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Evaluation Methodology

For each phrase, randomly select a sentence from NIST dataset containing it

Substituted top 1 to 10 paraphrases for phrase

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Methodology

Manually evaluated resulting sentences 0: Clearly wrong; grammatically incorrect or does not preserve meaning 1: Minor grammatical errors (e.g., subject-verb disagreement; wrong tenses, etc.), or meaning largely preserved but not completely 2: Totally correct; grammatically correct and meaning is preserved

Correct: 1 and 2; Wrong: 0 Two evaluators; Kappa = 0.62 (substantial agree.)

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Methodology

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Phr. HTP SBPq1

q2

… … …q49

q50

q51

… …q100

HTP vs. SBP

p11 p21 p31 p41 p51 p61 p71 p81 p91 p101 p111 p121

p12 p22 p32 p42 p52

p149 p249p349p449p549p649p749p849

p11 p21 p31 p41 p51 p61 p71

p12 p22 p32

p149p249p349p449p549

p150 p250 p350p450p550 p650p750

p151 p251 p351p451p551 p651p751p851

p1100p2100p3100 p410

0p5100p6100p7100p8100

p951p1051 p1151p1251

0.71 0.53

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Phr. HTP SBPq1

q2

… … …q49

q50

q51

… …q100

HTP vs. SBP

p11 p21 p31 p41 p51 p61 p71 p81 p91 p101 p111 p121

p12 p22 p32 p42 p52

p149 p249p349p449p549p649p749p849

p11 p21 p31 p41 p51 p61 p71

p12 p22 p32

p149p249p349p449p549

p150 p250 p350p450p550 p650p750

p151 p251 p351p451p551 p651p751p851 p951p1051 p1151p1251

0.56 0.39

373

paraphrases per

system

p1100p2100p3100 p410

0p5100p6100p7100p8100

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Phr. HTP SBPq1

q2

… … …q49

q50

q51

… …q100

HTP vs. SBP

p11 p21 p31 p41 p51 p61 p71 p81 p91 p101 p111 p121

p12 p22 p32 p42 p52

p149 p249p349p449p549p649p749p849

p11 p21 p31 p41 p51 p61 p71

p12 p22 p32

p149p249p349p449p549

p150 p250 p350p450p550 p650p750

p151 p251 p351p451p551 p651p751p851 p951p1051 p1151p1251

483

paraphrases

0.54

p1100p2100p3100 p410

0p5100p6100p7100p8100

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Phr. HTP SBPq1

q2

… … …q49

q50

q51

… …q100

HTP vs. SBP

p11 p21 p31 p41 p51 p61 p71 p81 p91 p101 p111 p121

p12 p22 p32 p42 p52

p149 p249p349p449p549p649p749p849

p11 p21 p31 p41 p51 p61 p71

p12 p22 p32

p149p249p349p449p549

p150 p250 p350p450p550 p650p750

p151 p251 p351p451p551 p651p751p851 p951p1051 p1151p1251

0.53

p1100p2100p3100 p410

0p5100p6100p7100p8100

0.50

0.71

0.61

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Phr. HTP SBPq1

q2

… … …q49

q50

q51

… …q100

HTP vs. SBP

p11 p21 p31 p41 p51 p61 p71 p81 p91 p101 p111 p121

p12 p22 p32 p42 p52

p149 p249p349p449p549p649p749p849

p11 p21 p31 p41 p51 p61 p71

p12 p22 p32

p149p249p349p449p549

p150 p250 p350p450p550 p650p750

p151 p251 p351p451p551 p651p751p851 p951p1051 p1151p1251

0.54 0.39

p1100p2100p3100 p410

0p5100p6100p7100p8100975

paraphrases

0.32

373

paraphrases

492

paraphrases

0.43

420 correct

paraphrases

145 correct

paraphrases

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Timings

System Timing (secs/phrase)

HTP 48

SBP 468

Motivation Background Hitting Time Paraphraser Experiments Future Work

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Overview

Apply HTP to languages other than English Evaluate HTP impact on applications

e.g., improve performance of resource-sparse machine translation systems

Add more features etc.

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Future Work

HTP: a paraphrase system based on random walks Good paraphrases have smaller hitting times General graph Path length > 2 Incorporate domain knowledge

HTP outperforms state-of-the-art

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Conclusion