NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation Opendomain multiturn dialogue systems are booming in humanmachine interactions, which encourage to chat actively and freely in an intelligent natural way. Previous generative conversational models usually employ a single and deterministic encoderdecoder 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, noninitiative 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 Gaussiandistributed 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 MisesFisher (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 stateoftheart baselines. The human evaluation shows the proposed model can produce more informative and initiative responses actively.

Generating Diverse Conversation Responses by Creating and Ranking Multiple Candidates Abstract   This paper introduces our systems built for Track 2 of Dialog System Technology Challenge 7 (DSTC7). This challenge track aimed to evaluate the response generation methods using fully datadriven conversation models in a knowledgegrounded setting, where textual facts were provided as the knowledge for each contextresponse pair. The sequencetosequence models have achieved impressive results in machine translation and have also been widely used for endtoend generative conversation modelling. However, they tended to output dull and repeated responses in previous studies. Our work aims to promote the diversity of endtoend conversation response generation by adopting a twostage pipeline. 1) Create multiple responses for an input context together with its textual facts. At this stage, two different models are designed, i.e., a variational generative (VariGen) model and a retrievalbased (Retrieval) model. 2) Rank and return the most relevant response by training a topic coherence discrimination (TCD) model for calculating ranking scores. In our experiments, we demonstrated the effectiveness of the response ranking strategy and the external textual knowledge for generating better responses. According to the official evaluation results, our Retrieval and VariGen systems ranked first and second respectively among all participant systems on Entropy metrics which measured the objective diversity of generated responses. Besides, the VariGen system ranked second on NIST and METEOR metrics which measured the objective quality of generated responses.