Rapidly Deploying Grammar-Based Speech Applications with Active Learning and Back-off Grammars

Abstract

Grammar-based approaches to spoken language understanding are utilized to a great extent in industry, particularly when developers are confronted with data sparsity. In order to ensure wide grammar coverage, developers typically modify their grammars in an iterative process of deploying the application, collecting and transcribing user utterances, and adjusting the grammar. In this paper, we explore enhancing this iterative process by leveraging active learning with back-off grammars. Because the back-off grammars expand coverage of user utterances, developers have a safety net for deploying applications earlier. Furthermore, the statistics related to the back-off can be used for active learning, thus reducing the effort and cost of data transcription. In experiments conducted on a commercially deployed application, the approach achieved levels of semantic accuracy comparable to transcribing all failed utterances with 87% less transcriptions.