Comparison and Combination of Lightly Supervised Approaches for Language Portability of a Spoken Language Understanding System

Portability of a spoken dialogue system (SDS) to a new domain or a new language is a hot topic as it may imply gains in time and cost for building new SDSs. In particular in this paper we investigate several fast and efficient approaches for language portability of the spoken language understanding (SLU) module of a dialogue system. We show that the use of statistical machine translation (SMT) can reduce the time and the cost of porting a system from a source to a target language. For conceptual decoding, a state-of-the-art module based on conditional random fields (CRF) is used and a new approach based on phrase-based statistical machine translation (PB-SMT) is also evaluated. The experimental results show the efficiency of the proposed methods for a fast and low cost SLU language portability. In addition, we propose two methods to increase SLU robustness to translation errors. Overall, it is shown that the combination of all these approaches can further reduce the concept error rate. While most of the experiments in this paper deal with portability from French to Italian (given the availability of the Media French corpus and its subset manually translated into Italian), a validation of our methodology is eventually proposed in Arabic.