A bootstrapping approach for SLU portability to a new language by inducting unannotated user queries

Abstract

This paper proposes a bootstrapping method of constructing a new spoken language understanding (SLU) system in a target language by utilizing statistical machine translation given an SLU module in some source language. The main challenge in this work is to induct unannotated automatic speech recognition results of user queries in the source language collected through a spoken dialog system, which is under public test. In order to select candidate expressions from among erroneous translation results stemming from problems with speech recognition and machine translation, we use back-translation results to check whether the translation result maintains the semantic meaning of the original sentence. We demonstrate that the proposed scheme can effectively prefer suitable sentences for inclusion in the training data as well as help improve the SLU module for the target language.