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

TL;DR

This survey reviews publicly available datasets for data-driven dialogue systems, highlighting their characteristics, uses, transfer learning methods, and evaluation metrics to advance research in this field.

Contribution

It provides a comprehensive overview of datasets and methodologies for building data-driven dialogue systems, aiding researchers in dataset selection and evaluation.

Findings

Many datasets are available for dialogue system research.

Transfer learning between datasets shows promising results.

Evaluation metrics vary and are crucial for assessing dialogue quality.

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.