tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection

Abstract

We describe the grammar induction system for Spoken Dialogue Systems (SDS) submitted to SemEval’14: Task 2. A statistical model is trained with a rich feature set and used for the selection of candidate rule fragments. Posterior probabilities produced by the fragment selection model are fused with estimates of phrase-level similarity based on lexical and contextual information. Domain and language portability are among the advantages of the proposed system that was experimentally validated for three thematically different domains in two languages.