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Ensemble Statistical and Heuristic Models for Unsupervised Word Alignment


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Statistical word alignment models need large amounts of training data while they are weak in small-sized corpora. This paper proposes a new approach of an unsupervised hybrid word alignment technique using an ensemble learning method. This algorithm uses three base alignment models in several rounds to generate alignments. The ensemble algorithm uses a weighed scheme for resampling training data and a voting score to consider aggregated alignments. The underlying alignment algorithms used in this study include IBM Model 1, 2 and a heuristic method based on Dice measurement. Our experimental results show that by this approach, the alignment error rate could be improved by at least 15% for the base alignment models.
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Ensemble Statistical and Heuristic Models
for Unsupervised Word Alignment
Mahsa Mohaghegh
Department of Computing
Auckland, New Zealand
Hossein Sarrafzadeh
Department of Computing
Auckland, New Zealand
Mehdi Mohammadi
Department of Computer Science
Western Michigan University
Abstract—Statistical word alignment models need large
amounts of training data while they are weak in small-sized
corpora. This paper proposes a new approach of an unsupervised
hybrid word alignment technique using an ensemble learning
method. This algorithm uses three base alignment models in
several rounds to generate alignments. The ensemble algorithm
uses a weighed scheme for resampling training data and a voting
score to consider aggregated alignments. The underlying
alignment algorithms used in this study include IBM Model 1, 2
and a heuristic method based on Dice measurement. Our
experimental results show that by this approach, the alignment
error rate could be improved by at least 15% for the base
alignment models.
Keywords—statistical word alignment; ensemble learning;
heuristic word alignment
I. I
As the main application of Natural Language Processing
(NLP), Machine Translation (MT) is becoming a necessary
tool in today’s rapid and voluminous stream of digital content.
This need could be better addressed by increasing cross-
regional communication as well as information exchange. For
example, many TV channels broadcast with closed caption to
different nations who have different languages. Another
example is some communities like the European Union
require documents to be translated in several languages
Statistical Machine Translation (SMT) is the dominant
approach for machine translation systems in recent years, and
is attracting more attention from researchers due to its
improvement and development. However, one prominent
problem in this field is word alignment of bilingual training
data. Word alignment can simply be defined as mapping
source language words to their corresponding translation
words in the target language. In a professional view, a word
alignment is an applicable hidden parameter in Statistical
Machine Translation [1]. This problem would be more
challenging when the underlying resources for the training
models are limited.
Based on Och and Ney [1], a general definition of
alignment between two word strings of source and target
languages can be represented by a subset of the Cartesian
product of the word positions in both word strings. However,
because of the difficulty in implementing such general
models, most alignment models are restricted in some way.
One typical approach is One-to-One alignment [2] in a
sentence pair (F= f
, E= e
) in which I and J are the
length of the source and target sentences in terms of words,
respectively. The alignment A would be represented as a
subset of {1,2, ..., I}×{1,2, ...,J}. A source word in the
position i is mapped to a word of target language in position j,
if (i, j) A. Mappings in this model may contain assignment
to an empty string in the target language as well.
The statistical alignment models are the basis of statistical
translation models and were initially word-based. IBM Models
1-5 [3], HMM [4] and Model 6 [1] are some remarkable
instances of this category. In the scope of bilingual term
extraction and dictionary construction, sequence-based models
(IBM models 1, 2 and HMM) are more attractive compared to
fertility-based models (like Model 3 and thereafter), since
sequence-based models are simple and fast and their
implementation is not as complex as fertility-based models
An important factor of good quality word alignment is a
huge amount of bilingual sentences. However this resource is
not typically available for any language pair. One promising
approach that has proven to yield reasonable results with both
limited data and large data sets is ensemble learning.
However, it has been proven that weak learners performing
only somewhat better than random can be combined to create
a stronger ensemble learner [6]. In this approach several
learners, known as weak learners, work over the same training
data and their results are aggregated to produce the final
output. Ensemble learning has been used in word alignment
[7-11] as well as SMT systems [12-13][15-17].
Recent research tends to combine several machine
translation systems with different levels of strength to improve
translation quality. The idea is that stronger systems are able
to cover the deficiency of weaker systems [12]. In other
words, errors can be addressed by the correct prediction of
other systems. This can be realized by generating translation
using a voting algorithm in a set of relatively close translation
outputs. This idea could be applied to word alignment
problems as well.
In this paper, we propose a new ensemble approach to
achieve some improvements in word alignment problems for
low-resource languages. Our approach is based on employing a
combination of three different word aligners, two of them
based on statistical models and one based on a heuristic model.
Then we resample training data for these algorithms to have
several weak word alignment learners. Then the results of these
weak learners are combined together to produce the final
The rest of the paper is arranged as follows: In the next
section, some related research that uses ensemble learning in
the word alignment domain is reviewed. In section III, the
underlying technology and algorithms which have been used in
this research are presented. Sections IV and V explain the
proposed approach and the experimental results respectively.
Finally, section VI concludes the paper.
Combination of multiple aligners has been studied and
reported increasingly in recent research. In [5], the authors
propose a model using two asymmetric word alignment
models to build a stronger symmetric word aligner. Their
approach is based on training each of the models IBM Model
1, Model 2 and HMM in both directions and considering
intersection of generated alignments. They reportedly achieve
up to 29% of reduction in Alignment Error Rate (AER), while
their experiments did not gain a remarkable BLEU [18] score
compared to a baseline system.
In [7], Wu and Wang proposed an ensemble learning
method to improve word alignment based on bagging and
cross-validation committees. They have used variations of
these methods by exploiting weighted and unweighted voting.
For their statistical word aligner, they used IBM Model 4.
They also used two direction word alignments to overcome
the problem of multi-word alignment. To give weight to the
alignments, they measured the association of the source unit
and the target unit in an alignment using a Dice coefficient
relationship. Their experiments with an English-Chinese
corpus showed that making an ensemble of aligners combined
with weighted voting obtain much lower error rate – up to
7.4% better than the baseline system.
Wu and Wang developed their work [8] with the AdaBoost
algorithm. In this research, they construct an alignment
reference set automatically using intersection of bidirectional
alignments obtained by IBM Model 4 over whole training
data. To be eligible to be added to the reference set, a word
alignment link must have a translation probability above a
certain threshold as well as its occurring frequency. It has
been shown in their results that word alignment is improved
using boosting rather than the original word aligner. Their
method is able to reach a reduction of 10.28% in error rate for
the English to Chinese direction and 21.52% for the Chinese
to English direction.
Another research over using boosting algorithms for word
alignment has been reported in [11]. Their work implements a
revised version of a boosting algorithm that relies on
unsupervised learning and puts more concentration on
sentence pairs that are identified as well-aligned. They use per
link Viterbi alignment probability to weigh sentence pairs in
each round of the boosting algorithm. They use IBM Model 4
as their base aligner and apply it in forward and backward
directions along with the current set of weights to obtain
alignments. Their experimental results compared to the
baseline system illustrate some improvement in BLEU score
as well as speed and phrase table size.
Xiao et. al. [12] focused on using an ensemble learning
method for statistical machine translation. In order to generate
the ensemble, they employ a pipeline of weak systems derived
from a single SMT engine. They investigate two ensemble
approaches: Bagging and Boosting. The training set for the
Bagging method comes from sampling over the whole training
data with replacement. For Boosting, the distribution over
training data is changed to weigh more on samples that
achieve a poor translation by weak systems. In their
experiments, they used Chinese–English translation with a
phrase-based, hierarchical phrase-based and syntax-based
translation system. Their results illustrated that using bagging
and boosting approaches outperforms in accuracy of
translation rather than baseline systems in terms of BLEU
Razmara and Sarkar [13] propose an ensemble learning
method based on stacking for SMT in which a base SMT
engine is used over a set of variations of training set generated
by a k-fold cross-validation method. In their proposed
approach, each of the k-1 folds are trained to produce a weak
learner system. Then these weak systems are combined
together to form the ensemble translator. They have reported
an improvement of up to 4 BLEU scores using this approach.
Statistical word alignment of a bilingual aligned corpus is a
core task of SMT. At the centre of these approaches a model
of the translation process is created in which the word
alignment is a hidden variable. Along with statistical models,
some heuristic models like Dice coefficient are also exploited
[19]. Computation of word alignments at these approaches are
based on analyzing some association score of a link between
the words of the source language and target language.
Each word mapping shows an association i j = a
which the alignment is between the source position i to the
target position j = a
. The alignment mappings may have some
association of a
=0 to indicate that there are no aligned words
in the target language for the source word. Here e
is a symbol
of empty words in the target language.
A. Statistical Alignment Models
Having a source language sentence f
and a target
language sentence e
, to model the relationship between the
source sentence and the target sentence in statistical machine
translation, we rely on the translation probability Pr(f
| e
In this model, a hidden parameter a=a
is introduced that
leads us to the alignment model Pr(f
, a
| e
) [14]. This
parameter reveals an association from a source position j to a
target position a
. The translation model and the alignment
model are related based on the following equation:
The statistical model is usually affected by some unknown
parameters θ which are revealed by learning from the training
data. The dependency of the model to the parameters could be
stated as the following equation:
 (2)
Using a parallel corpus consisting of S sentence pairs, we
can perform the training of unknown parameters θ. These
parameters are identified by likelihood maximization over the
training corpus:
We use IBM Model 1 and Model 2 as two base statistical
models. Model 1 is not affected by word order, while Model 2
uses word order in its probability. These models have a
different decomposition for Pr(f
, a
| e
) as expressed in
equation (4) and (5) for Model 1 and Model 2 respectively:
To determine this maximization in statistical models, one
useful tool is the EM algorithm [19]. There may be several
alignments for a sentence pair, but the best alignment is
always the desired one, given by:
 (6)
In order to acquire alignment distribution, EM only
considers the most likely word connections in the parameter
space and ignores the other less likely contributions [20]. At
the first step of EM algorithm, we build all possible
connections between words of each sentence pair. The point
here is that all connections are equally likely. Then we learn
from the corpus that some connections occur more frequently.
So, the inference would be that more frequent connections
results in more likely alignments. After calculating all
connection probabilities, the structure hidden in the parallel
corpus will be revealed and all source words will be aligned to
their counterparts in the target language.
B. Heuristic Models
In these models, a simple method for extracting word
alignments is used based on a similarity measurement between
the units of text of the two languages. In many cases, the Dice
coefficient is used for similarity measurement. All possible
association between the words of the source sentence and
those of the target sentence and their score are constructed:
At the above equation, C(e) shows the number of
occurrences of word e in the target sentences and C(f) is
associated to the count of words f in the source sentences. C(e,
f) represents the co-occurrence count of word e and word f in
the parallel corpus. Here, the word alignment could be
determined using the largest score:
[19] reports another version of this approach called
competitive linking algorithm in which after aligning highest
score associations, these alignments are eliminated from the
alignment matrix until every word in the source language or
those in the target language are aligned.
Unlike statistical models, heuristic models are simple to
develop as well as easy to understand. However, some results
show that the alignment quality of the Dice coefficient is lower
than the statistical models [19]. Och gained the alignment error
rate (AER) for the Dice model in the best case something about
30 percent. However, they demonstrated that statistical models
outperform the simple Dice algorithm. Despite this, it is
suitable for ensemble learning, since we need a learning
algorithm that performs better than chance, or in our case an
aligner that can align correctly more than 50% of alignments.
Our work is different from Wu et. al. [7] using the Dice
coefficient. They have used a custom version of the Dice
coefficient to compute the weight of each alignment link that
has been provided by the IBM Model 4, and used these weights
in an ensemble algorithm. Our work, however, relies on the
Dice coefficient just as a base of word the alignment engine to
generate word alignment links on training data.
C. AdaBoost Algorithm
If we have some learners where each of which can perform
slightly differently on a training data set, then by combining
them together it is possible to produce better results rather
than any of those learners individually. This is the main idea
behind ensemble learning.
The main algorithm of ensemble learning is AdaBoost
which is designed for supervised learning. This algorithm
assigns weights to samples based on the difficulty of previous
learners to classify the samples. These weights are part of the
input for training and are initialized to the same value, 1/N,
where N is the number of samples. Several learners are trained
over the training set in separate rounds. The weights are
updated by each learner based on the past results for each
training data obtained from previous learners. An error (e) is
computed at each iteration according to the summation of all
the samples that are misclassified. Then, weights of incorrect
predictions are modified by multiplying to α = e/(1-e), whereas
the weight of correct predictions remains unchanged. The most
important function in this algorithm is computing new weights,
which is performed by each learner in its round. Each learner
also checks the weights while it is performing classification.
There is another variation of AdaBoost that uses weights to
generate a subset of training data, and applies the learning
algorithm over that subset [6]. We adapted this approach into
the proposed algorithm.
Our Adaboost algorithm employs weights to generate a
sample from the whole training data, and trains over that
sample. Figure 1 shows the whole algorithm in detail. In each
iteration of the boosting algorithm, we use three base aligners:
IBM Model 1, Model 2, and the Dice coefficient. In each
round, based on the previous weight set for training sentence
pairs, a new subset of training set is considered for base
aligners to produce their alignments. Then they consent to an
alignment for each sentence pair by majority voting before
updating the weights. At the first round, all weights are set to a
same value: 1/N. The resampling module picks the data that
has weights greater than zero. In this way, all sentences are
contributed to the alignment process in the first iteration.
To update the weight of sentences in each round, we use a
sentence alignment confidence measurement. Huang [21]
defines alignment confidence measure as the geometric mean
of the alignment posterior probabilities in bidirectional
alignment models. However our alignment models are not
homogeneous, so we define an alignment confidence based on
voted alignments in each sentence pair.
Suppose for sentence pair (S, T), A
{(i, j) | 1 i I, 1 j
J } is the alignment set generated by model k in which I and
J are the length of the source and target sentences
respectively. We combine these alignment sets in such a way
that keeps the voting count (frequency) of each alignment link.
The resulting set looks like AT {((i, j), f) | 1 i I, 1 j J, f
1 }. By considering M as the size of this set for the sentence
pair (S, T), sentence alignment confidence is then defined as:
  
in which L is the total number of rounds in the ensemble
algorithm and D is the number of alignment models (in our
case D = 3). In the best case, when all the alignment sets for a
sentence pair are identical, the alignment confidence,
Confidence(S, T), should be equal to 1. Otherwise, when the
consensus between models decreases, the confidence
decreases as well. We use complement equation of confidence
score (alignment uncertainty) as a factor of updating weights
for the sentence pairs. Based on this factor, the ensemble
algorithm will concentrate on sentence pairs which have not
been aligned well:
An error rate of the alignment
is calculated in each round in
which the sentences whose alignment uncertainty is greater
than 0.5 are considered. Weights of each sentence pair will be
updated two times in each round. In the first instance, a pair’s
last weight, uncertainty, and model error rate will compute the
new weight. At the second time, the average of updated
weights is computed and the new weight of each pair is
computed based on the distance of each weight to the average
weight (). If the distance is positive, a new weight will be
updated to that, otherwise the new weight will be set to zero.
This means, in the next round, the sentence pair with weight 0
will not be picked up to participate in the training process.
After several iterations of the ensemble algorithm, and when
the learning process terminates, the final alignment selection
for each sentence pair is done based on a voting score that is
computed as the equation 11:
Figure 1. Proposed Alignment Ensemble Algorithm
In the above equation, R is the number of ensemble rounds in
which sentence pair (S, T) has been selected for alignment, h
is the number of times that alignment (i, j) appeared in R
rounds, a
is the sum of the votes for alignment (i, j) that are
greater than half of the number of models, and b
is the total
sum of the votes for alignment (i, j). The alignments that
obtain a score equal or greater than 0.5 are eligible to be or at
least be considered as final alignments.
V. E
We evaluated a proposed word alignment over a sentence
aligned Maori-English corpus which was prepared manually
during this research. However, since Maori has very limited
bilingual resources, we were only able to collect about 650
sentence pairs. The English side has 8173 words and 52689
characters while the Maori side has 10545 words and 51590
characters. Among these, we selected 50 sentence pairs as our
test data and aligned them manually to produce a reference
set. The remainder data is used to train the proposed
We used the evaluation scheme of Wu and Wang [8] to
evaluate the proposed ensemble alignment. Showing the
alignment set produced by the proposed algorithm by S, and
reference alignment set by R, the evaluation metrics will be as
The total number of alignments in the reference set is 526.
The ensemble algorithm generated 425 total alignments for test
data. In order to have a measure of the efficiency of this
approach, we performed two other separate experiments with
test data: one that just applies IBM Model 2 to generate
alignments and the other that just applies the Dice model for
word alignment generation. Table 1 presents the statistics of
the alignments generated by these three experiments.
Experiment Total alignments Correct alignments
Ensemble Model
IBM Model 2
Dice Model
The total number of alignments generated by the ensemble
method is somewhat less than the alignments generated by the
two other methods, and this is due to the voting scheme we
used in the output generation in the ensemble algorithm. On the
other hand, the total correct alignments have been increased by
this method for the same reason. Table 2 shows the precision,
recall, and AER for these models.
IBM Model 2
From Table 2, the ensemble model shows to achieve better
results in all metrics than IMB Model 2 and Dice model. It has
an improved alignment error rate by 15% and 22% compared
to IBM Model2 and Dice model respectively. Improvement in
precision and recall also has been gained by at least 34% and
32% respectively, compared to the two others.
In this paper, we present a new approach for word
alignment based on AdaBoost algorithm which uses statistical
and heuristic alignment models as well as a voting model to
produce the ensemble alignments of all underlying alignment
Our proposed approach demonstrates significant
improvement for alignment error rate despite training the
algorithm on a tiny set of bilingual sentence pairs. An obvious
consequence of having a small-sized training data is that the
alignment error rate will not be very low; however the point is
that having different alignment models improves the quality of
Comparison of the proposed weighting mechanism to other
weighting approaches is intended to be carried out in the next
phases of this work. Applying the proposed ensemble model to
a larger set of training sentence pairs and using the alignment
model in the context of statistical machine translation are the
other intended future works of this study.
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Full-text available
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State-of-the-art statistical machine trans-lation is based on alignments between phrases – sequences of words in the source and target sentences. The learn-ing step in these systems often relies on alignments between words. It is often as-sumed that the quality of this word align-ment is critical for translation. However, recent results suggest that the relation-ship between alignment quality and trans-lation quality is weaker than previously thought. We investigate this question directly, comparing the impact of high-quality alignments with a carefully con-structed set of degraded alignments. In or-der to tease apart various interactions, we report experiments investigating the im-pact of alignments on different aspects of the system. Our results confirm a weak correlation, but they also illustrate that more data and better feature engineering may be more beneficial than better align-ment.
Conference Paper
This paper proposes an approach to improve statistical word align- ment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addi- tion, we analyze the effect of different sizes of training sets on the bagging method. Experimental results indicate that both bagging and cross-validation committees improve the word alignment results regardless of weighted voting or unweighted voting. Weighted voting performs consistently better than un- weighted voting on different sizes of training sets.