178 citations · 997 across the 30 of their papers we have counts for
6 papers · 1 filter
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao +3
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawi…
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao +3
This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completio…
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li +4
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example,…
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Xiujun Li, Yun-Nung Chen, Lihong Li +2
Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performanc…
End-to-End Task-Completion Neural Dialogue Systems
Xiujun Li, Yun-Nung Chen, Lihong Li +2
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream m…
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur +4
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utteranc…