Multilingual Spoken Language Understanding using graphs and multiple translations

Abstract

This is the author’s version of a work that was accepted for publication in Computer Speech and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech and Language, vol. 38 (2016). DOI 10.1016/j.csl.2016.01.002.In this paper, we present an approach to multilingual Spoken Language Understanding based on a process of generalization of multiple translations, followed by a specific methodology to perform a semantic parsing of these combined translations. A statistical semantic model, which is learned from a segmented and labeled corpus, is used to represent the semantics of the task in a language. Our goal is to allow the users to interact with the system using other languages different from the one used to train the semantic models, avoiding the cost of segmenting and labeling a training corpus for each language. In order to reduce the effect of translation errors and to increase the coverage, we propose an algorithm to generate graphs of words from different translations. We also propose an algorithm to parse graphs of words with the statistical semantic model. The experimental results confirm the good behavior of this approach using French and English as input languages in a spoken language understanding task that was developed for Spanish. (C) 2016 Elsevier Ltd. All rights reserved.This work is partially supported by the Spanish MEC under contract TIN2014-54288-C4-3-R and by the Spanish MICINN under FPU Grant AP2010-4193.Calvo Lance, M.; Hurtado Oliver, LF.; García-Granada, F.; Sanchís Arnal, E.; Segarra Soriano, E. (2016). Multilingual Spoken Language Understanding using graphs and multiple translations. Computer Speech and Language. 38:86-103. https://doi.org/10.1016/j.csl.2016.01.002S861033