The main goal of this thesis is to develop computer assisted translation and
machine translation systems which present a more robust synergy with their
potential users. Hence, the main purpose is to make current state-of-the-art
systems more ergonomic, intuitive and efficient, so that the human expert feels
more comfortable when using them. For doing this, different techniques are
presented, focusing on improving the adaptability and response time of the
underlying statistical machine translation systems, as well as a strategy
aiming at enhancing human-machine interaction within an interactive machine
translation setup.  All of this with the ultimate purpose of filling in the
existing gap between the state of the art in machine translation and the final
tools that are usually available for the final human translators.

Concerning the response time of the machine translation systems, a parameter
pruning technique is presented, whose intuition stems from the concept of
bilingual segmentation, but which evolves towards a full parameter
re-estimation strategy. By using such strategy, experimental results presented
here prove that it is possible to achieve reductions of up to 97% in the number
of parameters required without a significant loss in translation quality. Being
robust across different language pairs, these results evidence that the pruning
technique presented is effective in a traditional machine translation scenario,
and could be used for instance in a post-editing setup.  Nevertheless,
experiments carried out within a simulated interactive machine translation
environment are slightly less convincing, since a trade-off between response
time and translation quality is needed.

Two orthogonally different approaches are presented with the purpose of
increasing the adaptability of the statistical machine translation systems. On
the one hand, we investigate how to increase the adaptability of the language
model, by subdividing it into several smaller language models which are then
interpolated in translation time according to the source sentence to be
translated. The specific sub-models are built either by taking advantage of
supervised information present in certain bilingual corpora, or by performing
unsupervised clustering on the training set, with the aim of uncovering
specific sub-topics or language styles present. On the other hand, Bayesian
predictive adaptation is elucidated as an efficient strategy for adapting the
translation models present in state-of-the-art machine translation systems.
Although adaptation experiments are only performed within the traditional
machine translation framework, the results obtained are compelling enough for
implementing them within an interactive setup, and such work will be done in
the near future. Nevertheless, it should be noted that the techniques developed
may be readily implemented within a computer assisted translation scenario, in
which a statistical machine translation system is providing the translations
that the user needs to modify and validate.

Finally, special attention is devoted to increasing the synergy between the
human expert and the interactive machine translation system. With this purpose,
two different forms of weaker feedback are studied, which intend to increase
the productivity of the human translator. For doing this, two different changes
to the traditional interaction scheme are presented. The first one aims at
anticipating the user's actions, and the second one is targeted at increasing
the flexibility of the system whenever the user signals that there is an error
he wants the system to correct.