Multilingual Spoken Language Understanding using graphs and multiple translations

•Test-on-source multilingual speech understanding.•Construction of graphs of words from multiple translations.•Semantic decoding of graphs of words using statistical models.•Unsupervised portability for Spoken Language Understanding. In this paper, we present an approach to multilingual Spoken Langu...

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Bibliographic Details
Published in:Computer speech & language 2016-07, Vol.38, p.86-103
Main Authors: Calvo, Marcos, Hurtado, Lluís-Felip, Garcia, Fernando, Sanchis, Emilio, Segarra, Encarna
Format: Article
Language:English
Subjects:
Algorithms
Graph of concepts
Graph of words
Graphs
Multilingual Language Understanding
Permissible error
Semantics
Speech
Statistical semantic models
Tasks
Training
Translations
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Summary:•Test-on-source multilingual speech understanding.•Construction of graphs of words from multiple translations.•Semantic decoding of graphs of words using statistical models.•Unsupervised portability for Spoken Language Understanding. 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.
ISSN:0885-2308
1095-8363
DOI:10.1016/j.csl.2016.01.002