Multilingual Spoken Language Understanding using graphs and multiple translations. (July 2016)

• Record Type: Journal Article
• Authors: Calvo, Marcos; Hurtado, Lluís-Felip; Garcia, Fernando; Sanchis, Emilio; Segarra, Encarna
• Is Part Of: Computer speech & language. Volume 38(2016)
• Page Start: 86
• Page End: 103
• Publication Date: 2016-07
• Subjects: Multilingual Language Understanding -- Graph of words -- Graph of concepts -- Statistical semantic models; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2016.01.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: 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. Abstract: 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.