MidGAN: Mutual information in GAN-based dialogue models

Despite a large amount of research into task-oriented dialogue systems, open-ended dialogue agents have received little attention. Recently, researchers have explored using Generative Adversarial Network (GAN) to build such models, however, these require extensive computation and data. This paper propose MidGAN to address prior limitations of GAN-based dialogue models. It does this by trying to maximize the mutual information of the responses generated by the model. To this end, we propose a new metric, MMI-Like. This is based on Maximizing Mutual Information (MMI), yet unlike MMI, does not rely on an auxiliary generative model. We evaluate MidGAN based on the diversity, informativeness by measuring similarity and relevance of the responses it generates by BLEU metric. Our evaluation results, based on the three benchmark datasets, show that MidGAN outperforms the existing state-of-the-art framework, ADV.