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:情感支持对话(ESC)任务可以利用各种支持战略来帮助人们缓解情绪痛苦并克服这些年来引起人们极大关注的问题。然而,大多数最先进的作品都严重依赖外部常识知识来推断每个对话回合中用户的精神状态。虽然有效,但它们可能受到人类大量努力、知识更新和领域长期变化的影响。因此,在本篇文章中,我们侧重于探索任务本身而不使用任何外部知识。我们发现所有现有作品都忽略了ESC的两个重要特征。 (a) 历史对话中存在着丰富的先前知识,例如对类似案例的响应和支持战略的总体顺序,这对当前对话具有很大的参考价值。 (b) 环境与支持战略之间有一个一对一对一的绘图关系, 也就是说, 多重战略对于单一背景来说是合理的。 考虑到这两个关键因素,我们提议了前知识增强情感支持模式, 并且具有潜在的变量变量, 之后, 拟议的模型充分挖掘了先前知识的模型, 将前一个可变式的 变式的模型 引入了我们之前的变式的变式数据序列。