NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation
Jinxin Chang, Ruifang He, Haiyang Xu, Kun Han, Longbiao Wang, Xiangang Li, Jianwu Dang · 2019
Open-domain multi-turn dialogue systems are booming in human-machine interactions, which encourage to chat actively and freely in an intelligent natural way. Previous generative conversational models usually employ a single and deterministic encoder-decoder framework to model the semantic consistency between the context and corresponding response. However, they neglect the various dialog patterns (we denote the regularity of topic shifting as dialog pattern) in the conversations, leading to uninformative, non-initiative yet plausible responses. Although the existing variational methods have improved the response diversity to some extent by introducing a global variability into the generative process, they fail to simulate the transfer between topics with directional information due to the weak interpretability of the Gaussian-distributed latent variables. In this paper, we propose a novel Neural Variational Scaling Reasoning Network (NVSRN) for initiative response generation. To this end, our approach has two core ingredients: neural dialog pattern reasoner (reasoner) and topic scaling mechanism. Specifically, inspired by the advantage of von Mises-Fisher (vMF) distribution modeling the directional data (e.g., the topic transfer state), we employ it as the latent space of the reasoner to explore the regularity of topic shifting, which is then used to reason the topic of response. Based on this, a topic scaling mechanism is designed to control the transfer degree of topic in the response generator. The experimental results on two large dialog datasets demonstrate that the proposed model outperforms state-of-the-art baselines. The human evaluation shows the proposed model can produce more informative and initiative responses actively.