Empathetic response from the therapist is key to the success of clinical psychotherapy, especially motivational interviewing. Previous work on computational modelling of empathy in motivational interviewing has focused on offline, session-level assessment of therapist empathy, where empathy captures all efforts that the therapist makes to understand the client's perspective and convey that understanding to the client. In this position paper, we propose a novel task of turn-level detection of client need for empathy. Concretely, we propose to leverage pre-trained language models and empathy-related general conversation corpora in a unique labeller-detector framework, where the labeller automatically annotates a motivational interviewing conversation corpus with empathy labels to train the detector that determines the need for therapist empathy. We also lay out our strategies of extending the detector with additional-input and multi-task setups to improve its detection and explainability.
翻译:心理治疗师的同情反应是临床心理治疗成功的关键,特别是激励性面谈。 以往在激励性面谈中对同情的计算建模工作一直侧重于对治疗师同情的离线、会议一级的评估,在这种评估中,同情感捕捉了治疗师为理解客户的观点和向客户传达这种理解而做出的所有努力。在本立场文件中,我们提议了一项新的任务,即对客户需要的同情感进行转基因检测。具体地说,我们提议在一个独特的标签-检测框架中,利用预先培训的语言模型和与同情性相关的一般对话公司,在这种框架中,标签员自动通知一个带有同情性标签的激励性谈话内容,以培训确定治疗者同情需要的检测者。 我们还提出了扩大检测者的战略,增加投入和多任务设置,以提高检测和解释能力。