Exploiting multiple hypotheses for Multilingual Spoken Language Understanding

In this work, we present an approach for multilingual portability of Spoken Language Understanding systems. The goal of this approach is to avoid the effort of acquiring and labeling new corpora to learn models when changing the language. The work presented in this paper is focused on the learning of a specific translator for the task and the mechanism of transmitting the information among the modules by means of graphs. These graphs represent a set of hypotheses (a language) that is the input to the statistical semantic decoder that provides the meaning of the sentence. Some experiments in a Spanish task evaluated with input French utterances and text are presented. They show the good behavior of the system, mainly when speech input is considered.