  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.
