DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
arXiv:1710.03957
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.
accepted by IJCNLP 2017
References in corpus (3)
Cited by in corpus (9)
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- DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
- Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
- Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations
- Exploring the context of recurrent neural network based conversational agents