Showing papers by "Lawrence K. Saul published in 2017"
14 May 2017
TL;DR: This work proposes an end-to-end architecture for making spoken SQL querying effective and efficient and presents initial empirical results to understand the feasibility of such an approach and identify several open research questions and propose alternative solutions.
Abstract: Speech-based inputs have become popular in many applications on constrained device environments such as smartphones and tablets, and even personal conversational assistants such as Siri, Alexa, and Cortana. Inspired by this recent success of speech-driven interfaces, in this work, we consider an important fundamental question: How should one design a speech-driven system to query structured data? Recent works have studied new querying modalities like visual [4, 8], touch-based [3, 7], and natural language interfaces (NLIs) [5, 6], especially for constrained querying environments such as tablets, smartphones, and conversational assistants. The commands given by the user are then translated to the Structured Query Language (SQL). But conspicuous by its absence is a speech-driven interface for regular SQL or other structured querying. One might wonder: Why dictate structured queries and not just use NLIs or visual interfaces? From a practical standpoint, many users, including in the C-suite, enterprise, Web, and other domains are already familiar with SQL (even if only a subset of it) and use it routinely. A spoken SQL interface could help them speed up query specification, especially in constrained settings such as smartphones and tablets, where typing SQL would be painful. More fundamentally, there is a trade-off inherent in any query interface, as illustrated in Figure 1(A).
11 citations