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.

翻译：情绪支持对话（ESC）任务通过运用多种支持策略，帮助人们缓解情绪困扰并解决所面临的问题，近年来受到广泛关注。然而，大多数前沿工作高度依赖外部常识知识来推断每轮对话中用户的心理状态。尽管这些方法有效，但长期来看可能面临人工成本高、知识更新困难和领域迁移等问题。因此，本文聚焦于探索任务本身，无需使用任何外部知识。我们发现现有工作均忽略了ESC的两个重要特征：(a) 历史对话中存在丰富的先验知识，例如相似案例的回应及支持策略的通用顺序，对当前对话具有重要参考价值；(b) 上下文与支持策略之间存在一对多的映射关系，即单一上下文可对应多种合理策略，这为生成结果的多样性奠定了良好基础。基于这两个关键因素，我们提出基于先验知识增强的情绪支持模型PoKE（含潜变量）。该模型充分挖掘示例和策略序列中的先验知识潜力，并利用潜变量建模策略的一对多关系。此外，我们引入记忆架构将编码知识融入解码器。在基准数据集上的实验结果表明，PoKE在自动评估和人工评估中均优于现有基线模型。与使用外部知识的模型相比，PoKE在某些指标上仍有小幅提升。进一步实验证明，丰富的先验知识有助于生成高质量的情绪支持内容，而充分学习的潜变量对生成结果的多样性至关重要。