A data-driven spoken language understanding system

Abstract

This paper presents a purely data-driven spoken language understanding (SLU) system. It consists of three major components, a speech recognizer, a semantic parser, and a dialog act decoder. A novel feature of the system is that the understanding components are trained directly from data without using explicit semantic grammar rules or fully-annotated corpus data. Despite this, the system is nevertheless able to capture hierarchical structure in user utterances and handle long range dependencies. Experiments have been conducted on the ATIS corpus and 16.1% and 12.6% utterance understanding error rates were obtained for spoken input using the ATIS-3 1993 and 1994 test sets. These results show that our system is comparable to existing SLU systems which rely on either hand-crafted semantic grammar rules or statistical models trained on fully-annotated training corpora but it has greatly reduced build cost.