标题： 深度标注心理治疗中的治疗工作联盟 显示英文标题

标题： Deep Annotation of Therapeutic Working Alliance in Psychotherapy

摘要： 治疗性工作联盟是心理治疗效果的一个重要预测指标。 在实践中，工作联盟是通过一套评分问卷从由患者和治疗师填写的量表中估算出来的。 在这项工作中，我们提出了一种分析框架，可以直接从心理治疗会谈中的自然语言推断出治疗性工作联盟，在回合级别的解析度上使用深度嵌入模型如Doc2Vec和SentenceBERT模型。 每节心理治疗会话的记录可以实时从会话录音中转录和生成，这些嵌入的对话与工作联盟量表中陈述的分布式表示进行比较。 我们在一个真实世界的数据集中展示了这种方法的有效性，该数据集包含了超过950节针对焦虑、抑郁、精神分裂症和自杀倾向患者的治疗性会谈。这项方法能够描绘出患者和治疗师之间的一致性轨迹，并提供可解释性，从而为临床精神病学提供洞见。 我们相信这样一个框架可以为治疗师在访谈环节中关于对话质量提供及时反馈。 显示英文摘要

摘要： The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the therapists fill out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist regarding the quality of the conversation in interview sessions.