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

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• Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
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• Probing Neural Dialog Models for Conversational Understanding
• Whose Emotion Matters? Speaking Activity Localisation without Prior Knowledge
• Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
• SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
• Dialogue Response Ranking Training with Large-Scale Human Feedback Data
• An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
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• Proto: A Neural Cocktail for Generating Appealing Conversations
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• Designing Precise and Robust Dialogue Response Evaluators
• Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models
• Style is NOT a single variable: Case Studies for Cross-Style Language Understanding
• VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation
• SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training
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• Generative Adversarial Imitation Learning for Empathy-based AI
• Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging
• Fine-grained Conversational Decoding via Isotropic and Proximal Search
• A Multi-Turn Emotionally Engaging Dialog Model
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• Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
• A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling
• Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
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• Exploring the context of recurrent neural network based conversational agents
• Automatically Select Emotion for Response via Personality-affected Emotion Transition
• Medical Literature Mining and Retrieval in a Conversational Setting
• POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
• Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations
• Conversational Semantic Role Labeling
• Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
• Incorporating Relevant Knowledge in Context Modeling and Response Generation