hitting the right paraphrases in good time
DESCRIPTION
Hitting The Right Paraphrases In Good Time. Stanley Kok Dept. of Comp. Sci. & Eng. Univ. of Washington Seattle, USA. Chris Brockett NLP Group Microsoft Research Redmond, USA. Motivation Background Hitting Time Paraphraser Experiments Future Work. Overview. 2. Motivation - PowerPoint PPT PresentationTRANSCRIPT
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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