1 Hitting The Right Paraphrases In Good Time

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1 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

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1 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 2 Overview

Motivation
Background
Hitting Time Paraphraser
Experiments
Future Work 3 Overview

4 What’s a paraphrase of… Paraphrase
System “is on good terms with” “is friendly with”
“is a friend of”
… Query expansion
Document summarization
Natural language generation
Question answering
etc. Applications

5 What’s a paraphrase of…

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

8 E1 E2 G1 F2 E3 E4 (in check) (under control) G2 G3 (unter kontrolle) F1 Graphical View

9 Graphical View Path lengths > 2 Add nodes to represent domain knowledge G1 F2 G2 G3 F1 E1 E2 E3 E4

Motivation
Background
Hitting Time Paraphraser
Experiments
Future Work 10 Overview

A A Random Walk Begin at node A
Randomly pick neighbor n E F D B C 11

Random Walk Begin at node A
Randomly pick neighbor n
Move to node n E F D A 2 B C 12

Random Walk Begin at node A
Randomly pick neighbor n
Move to node n
Repeat E F D A B 2 C 13

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] 14 Hitting Time from node i to j

Finding Truncated Hitting Time By Sampling E F D 1 B C A A T=5 15

Finding Truncated Hitting Time By Sampling E F 4 A B C D A D T=5 16

Finding Truncated Hitting Time By Sampling 5 F D A B C E A D E T=5 17

Finding Truncated Hitting Time By Sampling E F 4 A B C D A D E D T=5 18

Finding Truncated Hitting Time By Sampling E 6 D A B C F A D E D F T=5 19

Finding Truncated Hitting Time By Sampling 5 F D A B C E A D E D F E T=5 20

Finding Truncated Hitting Time By Sampling A D E D F E T=5 E F D A B C hAD=1 hAE=2 hAF=4 hAA=0 hAB=5 hAC=5 21

Motivation
Background
Hitting Time Paraphraser
Experiments
Future Work 22 Overview

23 Hitting Time Paraphraser
(HTP) Phrase Tables English-German
English-French
German-French
etc. Phrase Paraphrases

24 Graph Construction

25 Graph Construction

BFS from query phrase up to depth d or up to max. number n of nodes
d = 6, n = 50,000 26 … … … … … … … … … Graph Construction

27 Graph Construction … … … … … … … … …

28 Graph Construction … … … … … … … … … 0.6

29 Graph Construction … … … … … … … … … 0.5 0.5

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

31 Add Ngram Nodes “achieve the goal” “achieve the aim” “reach the objective”

32 Add “Syntax” Nodes “whose goal is” “the aim is” “the objective is” “what goal” start with article end with be start with
interrogatives

33 Add Not-Substring-Of Nodes “reach the” “reach the aim” “reach the objective” “objective” not-substring-of

34 Feature Nodes ngram nodes “syntax” nodes not-substring nodes phrase nodes

Run m truncated random walks again
Rank paraphrases in increasing order of hitting times 35 Re-estimate Truncated Hitting Times

Motivation
Background
Hitting Time Paraphraser
Experiments
Future Work 36 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 37 Data

SBP system [Callison-Burch, EMNLP’08]
HTP with no feature node
HTP with bipartite graph 38 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 39 Evaluation Methodology

For each phrase, randomly select a sentence from NIST dataset containing it
Substituted top 1 to 10 paraphrases for phrase 40 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.) 41 Methodology

42 HTP vs. SBP

43 HTP vs. SBP 373
paraphrases per system

44 HTP vs. SBP 483
paraphrases 0.54

45 HTP vs. SBP 0.53 0.50

46 HTP vs. SBP 975
paraphrases 0.32 373
paraphrases 492
paraphrases 0.43 420 correct
paraphrases 145 correct
paraphrases

47 Timings

Motivation
Background
Hitting Time Paraphraser
Experiments
Future Work 48 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. 49 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 50 Conclusion

1 Hitting The Right Paraphrases In Good Time

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