论文标题
工作联盟变压器进行心理治疗对话分类
Working Alliance Transformer for Psychotherapy Dialogue Classification
论文作者
论文摘要
作为对心理治疗中治疗结果的预测度量，工作联盟在其纽带，任务和目标方面衡量了患者和治疗师的一致性。长期以来一直是患者和治疗师的自我评估报告估计的临床数量，我们认为，在每次治疗课程中转录的对话中，使用自然语言处理技术可以更好地表征工作联盟。在这项工作中，我们提出了一个基于变压器的分类模型工作联盟变压器（WAT），该模型具有心理状态编码器，它通过预测对话的嵌入将对话的嵌入转移到工作联盟的临床清单的嵌入空间中，从而渗透了工作联盟分数。我们在现实世界中的数据集中评估了我们的方法，其中有950多个治疗课程，焦虑，抑郁，精神分裂症和自杀患者，并在此序列分类中使用有关治疗状态的信息的经验优势。
As a predictive measure of the treatment outcome in psychotherapy, the working alliance measures the agreement of the patient and the therapist in terms of their bond, task and goal. Long been a clinical quantity estimated by the patients' and therapists' self-evaluative reports, we believe that the working alliance can be better characterized using natural language processing technique directly in the dialogue transcribed in each therapy session. In this work, we propose the Working Alliance Transformer (WAT), a Transformer-based classification model that has a psychological state encoder which infers the working alliance scores by projecting the embedding of the dialogues turns onto the embedding space of the clinical inventory for working alliance. We evaluate our method in a real-world dataset with over 950 therapy sessions with anxiety, depression, schizophrenia and suicidal patients and demonstrate an empirical advantage of using information about the therapeutic states in this sequence classification task of psychotherapy dialogues.
