  Neural conversational models learn to generate responses by taking into
account the dialog history. These models are typically optimized over the
query-response pairs with a maximum likelihood estimation objective. However,
the query-response tuples are naturally loosely coupled, and there exist
multiple responses that can respond to a given query, which leads the
conversational model learning burdensome. Besides, the general dull response
problem is even worsened when the model is confronted with meaningless response
training instances. Intuitively, a high-quality response not only responds to
the given query but also links up to the future conversations, in this paper,
we leverage the query-response-future turn triples to induce the generated
responses that consider both the given context and the future conversations. To
facilitate the modeling of these triples, we further propose a novel
encoder-decoder based generative adversarial learning framework, Posterior
Generative Adversarial Network (Posterior-GAN), which consists of a forward and
a backward generative discriminator to cooperatively encourage the generated
response to be informative and coherent by two complementary assessment
perspectives. Experimental results demonstrate that our method effectively
boosts the informativeness and coherence of the generated response on both
automatic and human evaluation, which verifies the advantages of considering
two assessment perspectives.
