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Academic Paper

Title: Bootstrapping spoken dialogue systems by exploiting reusable libraries
Author: Giuseppe Di Fabbrizio
Institution: AT&T Labs – Research
Author: Gokhan Tur
Author: Dilek Hakkani-Tür
Institution: AT&T Labs – Research
Author: Mazin Gilbert
Institution: AT&T Labs – Research
Author: Bernard Renger
Institution: AT&T Labs – Research
Author: David Gibbon
Institution: AT&T Labs – Research
Author: Zhu Liu
Institution: AT&T Labs – Research
Author: Bahzad Shahraray
Institution: AT&T Labs – Research
Linguistic Field: Computational Linguistics; Discipline of Linguistics
Abstract: Building natural language spoken dialogue systems requires large amounts of human transcribed and labeled speech utterances to reach useful operational service performances. Furthermore, the design of such complex systems consists of several manual steps. The User Experience (UE) expert analyzes and defines by hand the system core functionalities: the system semantic scope (call-types) and the dialogue manager strategy that will drive the human–machine interaction. This approach is extensive and error-prone since it involves several nontrivial design decisions that can be evaluated only after the actual system deployment. Moreover, scalability is compromised by time, costs, and the high level of UE know-how needed to reach a consistent design. We propose a novel approach for bootstrapping spoken dialogue systems based on the reuse of existing transcribed and labeled data, common reusable dialogue templates, generic language and understanding models, and a consistent design process. We demonstrate that our approach reduces design and development time while providing an effective system without any application-specific data.


This article appears IN Natural Language Engineering Vol. 14, Issue 3, which you can READ on Cambridge's site or on LINGUIST .

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