Chapter | 4 Natural Language and Dialogue Processing
85
mainly to enlarge the set of available data and to predict the behaviour
of the SDS in unseen situations.Among simulation methods presented
in the literature, one can distinguish between state-transition methods
as proposed in [54] and methods based on modular simulation envi-
ronments as described in [48, 58–60]. The first type of method is more
task-dependent as well as the hybrid method proposed in [85]. One
can also distinguish methods according to the level of abstraction in
which the simulation takes place. While [59, 86] models the dialog at
the acoustic level, most of other methods [48, 54, 58, 60, 85] remain
at the intention level, arguing that simulation of other levels can be
inferred from intentions.
4.5 CONCLUSION
In this chapter, we have described processing systems that are usu-
ally hidden to the user although essential for building speech- or
text-based interfaces. All of these systems are still being the topic
of intensive research and there exists room for improvement in per-
formance. Especially, data-driven methods for optimising end-to-end
systems from speech recognition to text-to-speech synthesis are being
investigated [87], albeit data collection and annotation is still a major
problem. What is more, language processing is still limited to domain-
dependent applications (such as troubleshooting, database access, etc)
and cross-domain or even cross-language methods are still far from
being available. Also, transfer of academic research into the industrial
world is still rare [88]. The search for efficiency often leads to hand-
crafted and system-directed management strategies that are easier to
understand and control.
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