Unsupervised structured semantic inference for spoken dialog reservation tasks

International audienceThis 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