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A Factored Discriminative Spoken Language Understanding for Spoken Dialogue Systems

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

This paper describes a factored discriminative spoken language understanding method suitable for real-time parsing of recognised speech. It is based on a set of logistic regression classifiers, which are used to map input utterances into dialogue acts. The proposed method is evaluated on a corpus of spoken utterances from the Public Transport Information (PTI) domain. In PTI, users can interact with a dialogue system on the phone to find intra- and inter-city public transport connections and ask for weather forecast in a desired city. The results show that in adverse speech recognition conditions, the statistical parser yields significantly better results compared to the baseline well-tuned handcrafted parser.

Keywords

spoken language understanding dialogue systems meaning representation 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics, Institute of Formal and Applied LinguisticsCharles University in PragueCzech Republic

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