论文标题
主管：通过深入强化学习的心理治疗治疗策略的NLP宣布的实时建议
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
论文作者
论文摘要
我们提出了一个建议系统，该系统在心理治疗课程中为治疗师提供治疗策略。我们的系统使用转交级评级机制，该机制通过计算评分清单的深层嵌入与患者所说的当前句子之间的相似性得分来预测治疗结果。该系统会自动转录连续的音频流，并将其分为患者和治疗师的转弯，并对其治疗工作联盟进行实时推理。对话对及其计算的工作联盟，然后将评分送入深度强化学习推荐系统中，其中会话被视为用户，主题被视为项目。除了评估现有心理治疗会议数据集中核心组成部分的经验优势之外，我们还证明了该系统在Web应用程序中的有效性。
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time. Our system uses a turn-level rating mechanism that predicts the therapeutic outcome by computing a similarity score between the deep embedding of a scoring inventory, and the current sentence that the patient is speaking. The system automatically transcribes a continuous audio stream and separates it into turns of the patient and of the therapist and perform real-time inference of their therapeutic working alliance. The dialogue pairs along with their computed working alliance as ratings are then fed into a deep reinforcement learning recommendation system where the sessions are treated as users and the topics are treated as items. Other than evaluating the empirical advantages of the core components on an existing dataset of psychotherapy sessions, we demonstrate the effectiveness of this system in a web app.
