Emotional support conversation (ESC) task can utilize various support
strategies to help people relieve emotional distress and overcome the problem
they face, which has attracted much attention in these years. However, most
state-of-the-art works rely heavily on external commonsense knowledge to infer
the mental state of the user in every dialogue round. Although effective, they
may suffer from significant human effort, knowledge update and domain change in
a long run. Therefore, in this article, we focus on exploring the task itself
without using any external knowledge. We find all existing works ignore two
significant characteristics of ESC. (a) Abundant prior knowledge exists in
historical conversations, such as the responses to similar cases and the
general order of support strategies, which has a great reference value for
current conversation. (b) There is a one-to-many mapping relationship between
context and support strategy, i.e.multiple strategies are reasonable for a
single context. It lays a better foundation for the diversity of generations.
Taking into account these two key factors, we propose Prior Knowledge Enhanced
emotional support model with latent variable, PoKE. The proposed model fully
taps the potential of prior knowledge in terms of exemplars and strategy
sequence and then utilizes a latent variable to model the one-to-many
relationship of strategy. Furthermore, we introduce a memory schema to
incorporate the encoded knowledge into decoder. Experiment results on benchmark
dataset show that our PoKE outperforms existing baselines on both automatic
evaluation and human evaluation. Compared with the model using external
knowledge, PoKE still can make a slight improvement in some metrics. Further
experiments prove that abundant prior knowledge is conducive to high-quality
emotional support, and a well-learned latent variable is critical to the
diversity of generations.

本文提出了一种基于先前知识增强的情感支持模型，利用记忆模式将编码的知识纳入解码器，并利用潜在变量来建模策略的一对多关系，并展示了其在自动评估和人类评估方面的优越性。