We propose an adversarial learning approach for generating multi-turn
dialogue responses. Our proposed framework, hredGAN, is based on conditional
generative adversarial networks (GANs). The GAN's generator is a modified
hierarchical recurrent encoder-decoder network (HRED) and the discriminator is
a word-level bidirectional RNN that shares context and word embeddings with the
generator. During inference, noise samples conditioned on the dialogue history
are used to perturb the generator's latent space to generate several possible
responses. The final response is the one ranked best by the discriminator. The
hredGAN shows improved performance over existing methods: (1) it generalizes
better than networks trained using only the log-likelihood criterion, and (2)
it generates longer, more informative and more diverse responses with high
utterance and topic relevance even with limited training data. This improvement
is demonstrated on the Movie triples and Ubuntu dialogue datasets using both
automatic and human evaluations.

本文提出了一种对话生成的敌对学习方法，基于条件生成式敌对网络，生成器是修改后的层次循环编码器 - 解码器网络，鉴别器是与生成器共享上下文和词嵌入的字级双向循环神经网络，在 Ubuntu 对话数据集和电影三元组数据集上表现出更好的性能和更长、更丰富、更具相关性的自动生成回应。