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The key challenge of generative Visual Dialogue (VD) systems is to respond tohuman queries with informative answers in natural and contiguous conversationflow. Traditional Maximum Likelihood Estimation (MLE)-based methods only learnfrom positive responses but ignore the negative responses, and consequentlytend to yield safe or generic responses. To address this issue, we propose anovel training scheme in conjunction with weighted likelihood estimation (WLE)method. Furthermore, an adaptive multi-modal reasoning module is designed, toaccommodate various dialogue scenarios automatically and select relevantinformation accordingly. The experimental results on the VisDial benchmarkdemonstrate the superiority of our proposed algorithm over otherstate-of-the-art approaches, with an improvement of 5.81% on recall@10.

Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation