An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has proposed Next-Utterance-Classification (NUC) as a surrogate task for building dialogue systems from text data. In this paper we investigate the performance of humans on this task to validate the relevance of NUC as a method of evaluation. Our results show three main findings: (1) humans are able to correctly classify responses at a rate much better than chance, thus confirming that the task is feasible, (2) human performance levels vary across task domains (we consider 3 datasets) and expertise levels (novice vs experts), thus showing that a range of performance is possible on this type of task, (3) automated dialogue systems built using state-of-the-art machine learning methods have similar performance to the human novices, but worse than the experts, thus confirming the utility of this class of tasks for driving further research in automated dialogue systems.
Abstract Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human-machine interaction using spoken language. Spoken dialogu... Proper usability evaluations of spoken dialogue systems are costly and cumbersome to carry out. In this paper, we present a new approach for facilitating usability evaluations which is based on user error simulations. The idea is to replace real users with simulations derived from empirical observations of users’ erroneous behavior. The simulated errors must cover both system-driven errors (e.g., due to poor speech recognition) as well as conceptual errors and slips of the user, because neither alone is predictive of perceived usability. The simulation is integrated into a workbench which produces reports of typical and rare errors, and which allows usability ratings to be predicted. If successful, this workbench will help designers in making choices between system versions and lower testing costs at early phases of development. Challenges to the approach are discussed and solutions proposed. This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.