A Generative Adversarial Framework for Dialogue Generation with Neural Architecture Search

Yi Huang, Wei Hu, Junlan Feng

Abstract: Dialogue generation is a ambitious task that requires generating coherent and natural responses. In this paper, we propose a novel approach that utilizes a Seq2Seq generator enhanced by neural architecture search (NAS) on top of adversarial training to this end. Specifically, we apply the NAS for data native exploration, motivating the intrinsic characteristics for an adaptive structure. Further, we borrow the two-phase paradigm idea of pre-training and fine-tuning, extending the adversarial training process in terms of diversity and alignment. Experiments on the MultiWOZ-series datasets demonstrate our framework achieves superior performance in both automatic and human evaluations, and effectiveness is shown under low-resource settings. Comparative experiments indicate that the searched structure has a significantly faster convergence speed.