Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model

End-to-end dialogue generation has achieved promising results without using handcrafted features and attributes specific to each task and corpus. However, one of the fatal drawbacks in such approaches is that they are unable to generate informative utterances, so it limits their usage from some real-world conversational applications. In order to tackle this issue, this paper attempts to generate diverse and informative responses with a variational generation model, which contains a joint attention mechanism conditioning on the information from both dialogue contexts and extra knowledge. The experiments on benchmark DSTC7 data show that the proposed method generates responses with more grounded knowledge and improve the diversity of generated language. (c) 2020 Elsevier Ltd. All rights reserved.