Empirically Evaluating an Adaptable Spoken Dialogue System

TL;DR

This paper empirically evaluates TOOT, an adaptable spoken dialogue system for train schedule retrieval, demonstrating that adaptation improves performance depending on initial strategies.

Contribution

It introduces and empirically tests an adaptable dialogue system, showing how adaptation enhances user interaction over non-adaptive versions.

Findings

Adaptable TOOT outperforms non-adaptable TOOT in various measures.

The effectiveness of adaptation depends on initial dialogue strategies.

Empirical data from 80 dialogues support the benefits of system adaptability.

Abstract

Recent technological advances have made it possible to build real-time, interactive spoken dialogue systems for a wide variety of applications. However, when users do not respect the limitations of such systems, performance typically degrades. Although users differ with respect to their knowledge of system limitations, and although different dialogue strategies make system limitations more apparent to users, most current systems do not try to improve performance by adapting dialogue behavior to individual users. This paper presents an empirical evaluation of TOOT, an adaptable spoken dialogue system for retrieving train schedules on the web. We conduct an experiment in which 20 users carry out 4 tasks with both adaptable and non-adaptable versions of TOOT, resulting in a corpus of 80 dialogues. The values for a wide range of evaluation measures are then extracted from this corpus. Our results show that adaptable TOOT generally outperforms non-adaptable TOOT, and that the utility of adaptation depends on TOOT's initial dialogue strategies.