getting the structure right for word alignment: leaf alexander fraser and daniel marcu presenter qin...
DESCRIPTION
The generative story Source word Head words Links to zero or more non-head words (same side) Non-head words Linked from one head word (same side) Deleted words No link in source side Target words Head words Links to zero or more non-head words (same side) Non-head words Linked from one head word (same side) Spurious words No link in target sideTRANSCRIPT
Getting the structure right for word alignment: LEAF
Alexander Fraser and Daniel Marcu
Presenter Qin Gao
Problem
IBM Models have 1-N
assumption
Solutions
A sophisticated
generative story
Generative Estimation of parametersAdditional Solution
Decompose the model
components
Semi-supervised
training
ResultSignificant
Improvement on BLEU (AR-
EN)
Quick summary
The generative storySource word
Head words Links to zero or more non-head words (same side)
Non-head words
Linked from one head word (same side)
Deleted words No link in source sideTarget words
Head words Links to zero or more non-head words (same side)
Non-head words
Linked from one head word (same side)
Spurious words
No link in target side
Minimal translational correspondence
The generative story
A B C
1a. Condition: Source word
A B C
1b. Determine source word class
A B C
2a. Condition on source classes
C(A) C(B) C(C)
2b. Determine links between head word and non-head words
C(A) C(B) C(C)
3a. Depends on the source head word
A B C
3b. Determine the target head word
A B C
X
4a. Conditioned on source head word and cept size
A B C
X
2
4b. Determine the target cept size
A B C
X
2
?
5a. Depend on the existing sentence length
A B C
X
2
?
5b. Determine the number of spurious target words
A B C
X
2
? ?
6a. Depend on the target word
A B C
X ? ?XYZ
6b. Determine the spurious word
A B C
X ? ZXYZ
7a. Depends on target head word’s class and source word
A B C
C(X) ? Z
7b. Determine the non-head word it linked to
A B C
C(X) Y Z
8a. Depends on the classes of source/target head words
C(A) B C
C(X) Y Z
1 2 3
2
8b. Determine the position of target head word
C(A) B C
C(X)
Y Z
1 3
2
8c. Depends on the target word class
C(A) B C
C(X)
Y Z
1 3
32
8d. Determine the position of non-headwords
C(A) B C
C(X) Y
Z
1
1 32
9. Fill the vacant position uniformly
C(A) B C
C(X) YZ
1 32
(10) The real alignment
C(A) B C
C(X) YZ
Unsupervised parameter estimation
Bootstrap using HMM alignments in two directions Using the intersection to determine
head words Using 1-N alignment to determine target
cepts Using M-1 alignment to determine
source cepts Could be infeasible
Training: Similar to model 3/4/5
From some alignment (not sure how they get it), apply one of the seven operators to get new alignments
Move French non-head word to new head, move English non-head word to new head, swap heads of two French non-head words, swap heads of two English non-head words, swap English head word links of two French head
words, link English word to French word making new head
words, unlink English and French head words.
All the alignments that can be generated by one of the operators above, are called neighbors of the alignment
Training If we have better alignment in the
neighborhood, update the current alignment
Continue until no better alignment can be found
Collect count from the last neighborhood
Semi-supervised training Decompose the components in the large formula
treat them as features in log-linear model And other features
Used EMD algorithm (EM-Discriminative) method
Experiment First, a very weird operation, they
fully link alignments from ALL systems and then compare the performance
Training/Test Set
Experiments French/English: Phrase based Arabic/English: Hierarchical (Chiang
2005) Baseline: GIZA++ Model 4, Union Baseline Discriminative: Only using
Model 4 components as features
Conclusion(Mine) The new structural features are
useful in discriminative training No evidence to support the
generative model is superior over model 4
Unclear points Are F scores “biased?” No BLEU score given for LEAF
unsupervised They used features in addition to
LEAF features, where is the contribution comes from?