ABSTRACT

This thesis presents different contributions in the fields of
fully-automatic statistical machine translation and interactive
statistical machine translation.

In the field of statistical machine translation there are three problems
that are to be addressed, namely, the modelling problem, the training
problem and the search problem. In this thesis we present contributions
regarding these three problems.

Regarding the modelling problem, an alternative derivation of
phrase-based statistical translation models is proposed. Such derivation
introduces a set of statistical submodels governing different aspects of
the translation process. In addition to this, the resulting submodels
can be introduced as components of a log-linear model.

Regarding the training problem, an alternative estimation technique for
phrase-based models that tries to reduce the strong heuristic component
of the standard estimation technique is proposed. The proposed
estimation technique considers the phrase pairs that compose the phrase
model as part of complete bisegmentations of the source and target
sentences. We theoretically and empirically demonstrate that the
proposed estimation technique can be efficiently executed. Experimental
results obtained with the open-source Thot toolkit also presented in
this thesis, show that the alternative estimation technique obtains
phrase models with lower perplexity than those obtained by means of the
standard estimation technique. However, the reduction in the perplexity
of the model did not allow us to obtain improvements in the translation
quality.

To deal with the search problem, we propose a search algorithm which is
based on the branch-and-bound search paradigm. The proposed algorithm
generalises different search strategies that can be accessed by
modifying the input parameters. We carried out experiments to evaluate
the performance of the proposed search algorithm.

Additionally, we also study an alternative formalisation of the search
problem in which the best alignment at phrase-level is obtained given
the source and target sentences. To solve this problem, smoothing
techniques are applied over the phrase table. In addition to this, the
standard search algorithm for phrase-based statistical machine
translation is modified to explore the space of possible
alignments. Empirical results show that the proposed techniques can be
used to efficiently and robustly generate phrase-based alignments.

One disadvantage of phrase-based models is its huge size when they are
estimated from very large corpora. In this thesis, we propose techniques
to alleviate this problem during both the estimation and the decoding
stages. For this purpose, main memory requirements are transformed into
hard disk requirements. Experimental results show that the hard disk
accesses do not significantly decrease the efficiency of the SMT system.

With respect to the contributions in the field of interactive
statistical machine translation, on the one hand, we present alternative
techniques to implement interactive machine translation systems. On the
other hand, we give a proposal of an interactive machine translation
system which is able to learn from user-feedback by means of online
learning techniques.

We propose two alternative techniques for interactive statistical
machine translation. The first one is based on the generation of partial
alignments at phrase level. This approach constitutes an application of
the phrase-based alignment generation techniques that are also proposed
in this thesis. The second proposal tackles the interactive machine
translation process by means of word graphs and stochastic
error-correction models. The proposed approach differs from other
existing approaches described in the literature in the introduction of
error-correction techniques in the statistical framework of the
interactive machine translation process. We carried out experiments to
evaluate the two proposed techniques, showing that they are competitive
with state-of-the-art interactive machine translation systems. In
addition to this, such techniques have been used to implement an
interactive machine translation prototype following a client-server
architecture.

Finally, the above mentioned interactive machine translation system with
online learning is based on the use of statistical models that can be
incrementally updated. The main difficulty defining incremental versions
of the statistical models involved in the interactive translation
process appears when such models are estimated by means of the
expectation-maximisation algorithm. To solve this problem, we propose
the application of the incremental version of such algorithm. The
proposed interactive machine translation system with online learning was
empirically evaluated, demonstrating that the system is able to learn
from scratch or from previously estimated models. In addition to this,
the obtained results also show that the interactive machine translation
system with online learning significantly outperforms other
state-of-the-art systems described in the literature.