activity
20152023
most citedDeep Reinforcement Learning for Dialogue Generation

406 citations · 2.4k across the 39 of their papers we have counts for

collaborators
Showing 2019Show all

8 papers · 1 filter

cs.CL2019★ 53 cited

The Eighth Dialog System Technology Challenge

Seokhwan Kim, Michel Galley, Chulaka Gunasekara +18

This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a prag…

cs.CL2019★ 103 cited

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

Yizhe Zhang, Siqi Sun, Michel Galley +6

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…

cs.CL2019★ 7 cited

Structuring Latent Spaces for Stylized Response Generation

Xiang Gao, Yizhe Zhang, Sungjin Lee +4

Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate response…

cs.CL2019★ 18 cited

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Lianhui Qin, Michel Galley, Chris Brockett +5

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentf…

cs.CL2019★ 1 cited

Towards Content Transfer through Grounded Text Generation

Shrimai Prabhumoye, Chris Quirk, Michel Galley

Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on co…

cs.CL2019★ 7 cited

Jointly Optimizing Diversity and Relevance in Neural Response Generation

Xiang Gao, Sungjin Lee, Yizhe Zhang +4

Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the…

cs.CL2019★ 31 cited

Consistent Dialogue Generation with Self-supervised Feature Learning

Yizhe Zhang, Xiang Gao, Sungjin Lee +4

Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversat…

cs.CL2019★ 27 cited

Dialog System Technology Challenge 7

Koichiro Yoshino, Chiori Hori, Julien Perez +14

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dial…