DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

TL;DR

DailyDialog is a high-quality, manually labeled multi-turn dialogue dataset reflecting daily human communication, including emotion and intention annotations, designed to advance research in dialog systems.

Contribution

The paper introduces a new, carefully curated dataset with detailed annotations, filling a gap in resources for training and evaluating dialogue systems.

Findings

Existing approaches evaluated on DailyDialog

Dataset covers diverse daily topics

Annotations include emotion and intention labels

Abstract

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems.