  Generating qualitative responses has always been a challenge for
human-computer dialogue systems. Existing dialogue systems generally derive
from either retrieval-based or generative-based approaches, both of which have
their own pros and cons. Despite the natural idea of an ensemble model of the
two, existing ensemble methods only focused on leveraging one approach to
enhance another, we argue however that they can be further mutually enhanced
with a proper training strategy. In this paper, we propose ensembleGAN, an
adversarial learning framework for enhancing a retrieval-generation ensemble
model in open-domain conversation scenario. It consists of a
language-model-like generator, a ranker generator, and one ranker
discriminator. Aiming at generating responses that approximate the ground-truth
and receive high ranking scores from the discriminator, the two generators
learn to generate improved highly relevant responses and competitive unobserved
candidates respectively, while the discriminative ranker is trained to identify
true responses from adversarial ones, thus featuring the merits of both
generator counterparts. The experimental results on a large short-text
conversation data demonstrate the effectiveness of the ensembleGAN by the
amelioration on both human and automatic evaluation metrics.
