27 citations · 53 across the 4 of their papers we have counts for
7 papers
An Adversarially-Learned Turing Test for Dialog Generation Models
Xiang Gao, Yizhe Zhang, Michel Galley +1
The design of better automated dialogue evaluation metrics offers the potential of accelerate evaluation research on conversational AI. However, existing trainable dialogue evaluat…
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Xiang Gao, Yizhe Zhang, Michel Galley +2
Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning m…
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
Chunyuan Li, Xiang Gao, Yuan Li +4
When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this…
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…
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…
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…