178 citations · 997 across the 30 of their papers we have counts for
8 papers · 1 filter
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Xiujun Li, Jingjing Liu +2
Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integratin…
Towards Coherent and Cohesive Long-form Text Generation
Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang +5
Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, fe…
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao +2
This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the D…
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao +4
Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strat…
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Ziyu Yao, Xiujun Li, Jianfeng Gao +2
Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the descrip…
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Xiujun Li, Yu Wang, Siqi Sun +3
This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate…