Combination of stochastic understanding and machine translation systems for language portability of dialogue systems

In this paper, several approaches for language portability of dialogue systems are investigated with a focus on the spoken language understanding (SLU) component. We show that the use of statistical machine translation (SMT) can greatly reduce the time and cost of porting an existing system from a source to a target language. Using automatically translated training data we study phrase-based machine translation as an alternative to conditional random fields for conceptual decoding to compensate for the loss of a precise concept-word alignment. Also two ways to increase SLU robustness to translation errors (smeared training data and translation post-editing) are shown to improve performance when test data are translated then decoded in the source language. Overall the combination of all these approaches allows to reduce even further the concept error rate. Experiments were carried out on the French MEDIA dialogue corpus with a subset manually translated into Italian.