1 Quasi-Synchronous Grammars Based on key observations in MT: translated sentences often have some isomorphic syntactic structure, but not usually in entirety. the strictness of the isomorphism may vary across words or syntactic rules. Key idea: Unlike some synchronous grammars (e.g. SCFG, which is more strict and rigid), QG defines a monolingual grammar for the target tree, “inspired” by the source tree.
1 Quasi-Synchronous Grammars Based on key observations in MT: translated sentences often have some isomorphic syntactic structure, but not usually in.
Dec 19, 2015
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1
Quasi-Synchronous Grammars Based on key observations in MT:
translated sentences often have some isomorphic syntactic structure, but not usually in entirety.
the strictness of the isomorphism may vary across words or syntactic rules.
Key idea: Unlike some synchronous grammars (e.g. SCFG,
which is more strict and rigid), QG defines a monolingual grammar for the target tree, “inspired” by the source tree.
2
Quasi-Synchronous Grammars In other words, we model the generation of
the target tree, influenced by the source tree (and their alignment)
QA can be thought of as extremely free monolingual translation.
The linkage between question and answer trees in QA is looser than in MT, which gives a bigger edge to QG.
3
Model Works on labeled dependency parse trees Learn the hidden structure (alignment between Q and
A trees) by summing out ALL possible alignments
One particular alignment tells us both the syntactic configurations and the word-to-word semantic correspondences
An example…
question answer
answerparse tree
questionparse tree
an alignment
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
isVB
Q: A:$
root$
root
root root
subj with
nmod
nmod
root)|P(root
noNE)|P(noNE
VBD)| P(VB
Our model makes local Markov assumptions to allow efficient computation via Dynamic Programming (details in paper)
given its parent, a word is independent of all other words (including siblings).
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
isVB
Q: A:$
root$
root
root
subj
root
subj with
nmod
nmod
child)-parent|P(subj
person)|P(qword
NNP)|P(WP
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
Q: A:$
root$
root
root
subj obj
root
subj with
nmod
nmod
child)-tgrandparen|P(obj
noNE)|P(noNE
NN)|P(NN
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
Q: A:$
root$
root
root
subj obj
det
root
subj with
nmod
nmod
)word-same|P(det
noNE)|P(noNE
N)|P(DT
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
)child-parent|P(of
location)|P(location
JJ)|P(NNP
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6 types of syntactic configurations
Parent-child
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
Parent-child configuration
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6 types of syntactic configurations
Parent-child Same-word
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
Same-word configuration
Parent-child configuration
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6 types of syntactic configurations
Parent-child Same-word Grandparent-child
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
Parent-child configuration Same-word configuration
Grandparent-child configuration
20
6 types of syntactic configurations
Parent-child Same-word Grandparent-child Child-parent Siblings C-command(Same as [D. Smith & Eisner ’06])
Parent-child configuration Same-word configuration Grandparent-child configuration
Child-parent configuration Siblings configuration C-command configuration
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Modeling alignment Base model
)child-parent|P(of
location)|P(location
N)|P(N
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
BushNNP
person
metVBD
FrenchJJ
location
presidentNN
Jacques ChiracNNP
person
whoWP
qword
leaderNN
isVB
theDT
FranceNNP
location
Q: A:$
root$
root
root
subj obj
det of
root
subj with
nmod
nmod
25
Modeling alignment cont.
Base model
Log-linear modelLexical-semantic features from WordNet,Identity, hypernym, synonym, entailment, etc.
Mixture model
26
Parameter estimation
Things to be learnt Multinomial distributions in base model Log-linear model feature weights Mixture coefficient
Training involves summing out hidden structures, thus non-convex.
Solved using conditional Expectation-Maximization
27
Experiments
Trec8-12 data set for training Trec13 questions for development
and testing
28
Candidate answer generation
For each question, we take all documents from the TREC doc pool, and extract sentences that contain at least one non-stop keywords from the question.
For computational reasons (parsing speed, etc.), we only took answer sentences <= 40 words.
29
Dataset statistics Manually labeled 100 questions for training
Total: 348 positive Q/A pairs 84 questions for dev
Total: 1415 Q/A pairs 3.1+, 17.1-
100 questions for testing Total: 1703 Q/A pairs 3.6+, 20.0-
Automatically labeled another 2193 questions to create a noisy training set, for evaluating model robustness
30
Experiments cont.
Each question and answer sentence is tokenized, POS tagged (MX-POST), parsed (MSTParser) and labeled with named-entity tags (Identifinder)
31
Baseline systems (replications) [Cui et al. SIGIR ‘05]
The algorithm behind one of the best performing systems in TREC evaluations.
It uses a mutual information-inspired score computed over dependency trees and a single fixed alignment between them.
[Punyakanok et al. NLE ’04] measures the similarity between Q and A by
computing tree edit distance. Both baselines are high-performing, syntax-based,
and most straight-forward to replicate We further enhanced the algorithms by augmenting
them with WordNet.
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ResultsMean Average
PrecisionMean Reciprocal
Rank of Top 1
Statistically significantly better than the 2nd best score in each column
28.2% 23.9% 41.2% 30.3%
33
Summing vs. Max
34
Switching back
Tree-edit CRFs
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