A Survey of Available Corpora for Building Data-Driven Dialogue Systems

TLDR

Recent advances in speech and language understanding have largely been driven by data‑driven models, yet dialogue systems remain predominantly engineered with expert knowledge. This survey aims to catalog publicly available datasets for data‑driven dialogue system research. The authors analyze dataset characteristics, potential for learning diverse dialogue strategies, transfer‑learning methods, external knowledge integration, and suitable evaluation metrics. The survey finds that data‑driven approaches to dialogue systems are feasible and show promise.

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.