Neural Approaches to Conversational AI
Jianfeng Gao,Michel Galley,Lihong Li +2 more
- pp 1371-1374
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TLDR
This tutorial surveys neural approaches to conversational AI that were developed in the last few years, and presents a review of state-of-the-art neural approaches, drawing the connection between neural approaches and traditional symbolic approaches.Abstract:
This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.read more
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Neural Approaches to Conversational AI
TL;DR: In this article, the authors present a survey of state-of-the-art neural approaches to conversational AI, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.
Proceedings ArticleDOI
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang,Siqi Sun,Michel Galley,Yen-Chun Chen,Chris Brockett,Xiang Gao,Jianfeng Gao,Jingjing Liu,Bill Dolan +8 more
TL;DR: It is shown that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
Proceedings ArticleDOI
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Xin Wang,Qiuyuan Huang,Asli Celikyilmaz,Jianfeng Gao,Dinghan Shen,Yuan-Fang Wang,William Yang Wang,Lei Zhang +7 more
TL;DR: In this paper, a reinforcement learning-based approach is proposed to enforce cross-modal grounding both locally and globally via reinforcement learning (RL), where a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories.
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