Challenges in Building Intelligent Open-domain Dialog Systems

Abstract

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

References in corpus (22)

• Sequence to Sequence Learning with Neural Networks
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• Wizard of Wikipedia: Knowledge-Powered Conversational agents
• TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
• Towards a Human-like Open-Domain Chatbot
• A Simple, Fast Diverse Decoding Algorithm for Neural Generation
• An Information Retrieval Approach to Short Text Conversation
• Why We Need New Evaluation Metrics for NLG
• DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
• Personalized Dialogue Generation with Diversified Traits
• Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
• Proactive Human-Machine Conversation with Explicit Conversation Goals
• MatchZoo: A Toolkit for Deep Text Matching
• Consistent Dialogue Generation with Self-supervised Feature Learning
• Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
• Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
• Deep Retrieval-Based Dialogue Systems: A Short Review
• Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
• Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation
• Jointly Optimizing Diversity and Relevance in Neural Response Generation
• Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
• Finding Your Voice: The Linguistic Development of Mental Health Counselors

Cited by in corpus (6)

• Survey of Hallucination in Natural Language Generation
• Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey
• A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
• A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation
• A Survey of Document Grounded Dialogue Systems (DGDS)
• On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments