DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

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)

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Neural Responding Machine for Short-Text Conversation
• Coherent Dialogue with Attention-based Language Models

Cited by in corpus (12)

• Probing Neural Dialog Models for Conversational Understanding
• Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
• Dialogue Response Ranking Training with Large-Scale Human Feedback Data
• Designing Precise and Robust Dialogue Response Evaluators
• Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models
• Generating Dialogue Responses from a Semantic Latent Space
• DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
• Conversational Semantic Role Labeling
• Exploring the context of recurrent neural network based conversational agents
• Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations
• Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
• Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation