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Emotional support conversation (ESC) task can utilize various supportstrategies to help people relieve emotional distress and overcome the problemthey face, which has attracted much attention in these years. However, moststate-of-the-art works rely heavily on external commonsense knowledge to inferthe mental state of the user in every dialogue round. Although effective, theymay suffer from significant human effort, knowledge update and domain change ina long run. Therefore, in this article, we focus on exploring the task itselfwithout using any external knowledge. We find all existing works ignore twosignificant characteristics of ESC. (a) Abundant prior knowledge exists inhistorical conversations, such as the responses to similar cases and thegeneral order of support strategies, which has a great reference value forcurrent conversation. (b) There is a one-to-many mapping relationship betweencontext and support strategy, i.e.multiple strategies are reasonable for asingle context. It lays a better foundation for the diversity of generations.Taking into account these two key factors, we propose Prior Knowledge Enhancedemotional support model with latent variable, PoKE. The proposed model fullytaps the potential of prior knowledge in terms of exemplars and strategysequence and then utilizes a latent variable to model the one-to-manyrelationship of strategy. Furthermore, we introduce a memory schema toincorporate the encoded knowledge into decoder. Experiment results on benchmarkdataset show that our PoKE outperforms existing baselines on both automaticevaluation and human evaluation. Compared with the model using externalknowledge, PoKE still can make a slight improvement in some metrics. Furtherexperiments prove that abundant prior knowledge is conducive to high-qualityemotional support, and a well-learned latent variable is critical to thediversity of generations.

PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable