Language Modeling for Spoken Dialogue System based on Filtering using Predicate-Argument Structures

We present a novel scheme of language modeling for a spoken dialogue system by effectively filtering query sentences collected via a Web site of wisdom of crowds. Our goal is a speechbased information navigation system by retrieving from backend documents such as Web news. Then, we expect that users make queries that are relevant to the backend documents. The relevance measure can be defined with cross-entropy or perplexity by the language model generated from the documents in a conventional manner. In this article, we propose a novel criteria that considers semantic-level information. It is based on predicate-argument (P-A) pairs and their relevance to the documents (or topic) is defined by a naive Bayes score. Experimental evaluations demonstrate that the proposed relevance measure effectively selects relevant sentences used for a language model, resulting in significant reduction of the word error rate of speech recognition as well as the semantic-level error rate.