An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets

Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022)

Abstract

Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.