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Unsupervised structured semantic inference for spoken dialog reservation tasks

Alejandra Lorenzo 1 Lina M. Rojas-Barahona 1 Christophe Cerisara 1 
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This work proposes a generative model to infer latent semantic structures on top of manual speech transcriptions in a spoken dialog reservation task. The proposed model is akin to a standard semantic role labeling system, except that it is unsupervised, it does not rely on any syntactic information and it exploits concepts derived from a domain-specific ontology. The semantic structure is obtained with un- supervised Bayesian inference, using the Metropolis-Hastings sampling algorithm. It is evaluated both in terms of attachment accuracy and purity-collocation for clustering, and compared with strong baselines on the French MEDIA spoken-dialog corpus.
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Submitted on : Thursday, November 28, 2013 - 3:58:46 PM
Last modification on : Saturday, October 16, 2021 - 11:26:06 AM
Long-term archiving on: : Monday, March 3, 2014 - 6:16:34 PM


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  • HAL Id : hal-00911017, version 1



Alejandra Lorenzo, Lina M. Rojas-Barahona, Christophe Cerisara. Unsupervised structured semantic inference for spoken dialog reservation tasks. SIGDIAL - 14th annual SIGdial Meeting on Discourse and Dialogue - 2013, Aug 2013, Metz, France. pp.12-20. ⟨hal-00911017⟩



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