A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

TLDR

Data‑driven models have spurred breakthroughs in many speech and language tasks, yet dialogue systems largely remain engineered, though recent results suggest data‑driven approaches are feasible and promising. The study surveys publicly available datasets for data‑driven dialogue system learning to aid research. The authors examine dataset characteristics, their use for learning diverse dialogue strategies and secondary tasks, transfer‑learning techniques, external knowledge integration, and suitable evaluation metrics.

Abstract

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.