Neural networks have recently become good at engaging in dialog. However, current approaches are based solely on verbal text, lacking the richness of a real face-to-face conversation. We propose a neural conversation model that aims to read and generate facial gestures alongside with text. This allows our model to adapt its response based on the "mood" of the conversation. In particular, we introduce an RNN encoder-decoder that exploits the movement of facial muscles, as well as the verbal conversation. The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural. We train our neural network by having it "watch" 250 movies. We showcase our joint face-text model in generating more natural conversations through automatic metrics and a human study. We demonstrate an example application with a face-to-face chatting avatar.

我们提出了一个新的神经会话模型，其不仅读取和生成文本信息，还能够识别和生成面部表情，以及基于对话中的情绪适应其响应。该模型采用RNN编码器-解码器架构，通过观察电影进行训练，并通过自动度量和人类研究展示了其生成更自然对话的能力。

一种面对面的神经对话模型