In the wake of the post-pandemic era, marked by social isolation and surging rates of depression and anxiety, conversational agents based on digital psychotherapy can play an influential role compared to traditional therapy sessions. In this work, we develop a voice-capable chatbot in Farsi to guide users through Self-Attachment (SAT), a novel, self-administered, holistic psychological technique based on attachment theory. Our chatbot uses a dynamic array of rule-based and classification-based modules to comprehend user input throughout the conversation and navigates a dialogue flowchart accordingly, recommending appropriate SAT exercises that depend on the user's emotional and mental state. In particular, we collect a dataset of over 6,000 utterances and develop a novel sentiment-analysis module that classifies user sentiment into 12 classes, with accuracy above 92%. To keep the conversation novel and engaging, the chatbot's responses are retrieved from a large dataset of utterances created with the aid of Farsi GPT-2 and a reinforcement learning approach, thus requiring minimal human annotation. Our chatbot also offers a question-answering module, called SAT Teacher, to answer users' questions about the principles of Self-Attachment. Finally, we design a cross-platform application as the bot's user interface. We evaluate our platform in a ten-day human study with N=52 volunteers from the non-clinical population, who have had over 2,000 dialogues in total with the chatbot. The results indicate that the platform was engaging to most users (75%), 72% felt better after the interactions, and 74% were satisfied with the SAT Teacher's performance.

翻译：在后疫情时代，社会隔离与抑郁及焦虑发病率激增的背景下，基于数字心理治疗的对话智能体相较于传统治疗疗程可发挥关键作用。本研究开发了一个支持语音交互的波斯语聊天机器人，用于引导用户实践自我依恋技术——一种基于依恋理论的新型自主式整体心理技术。该聊天机器人采用基于规则与基于分类模块的动态组合方式，在对话全过程中理解用户输入，并依据对话流程图进行导航，根据用户情绪与心理状态推荐相应的SAT练习。具体而言，我们收集了超过6000条话语的数据集，并开发了新型情感分析模块，能将用户情感分类为12个类别，准确率超过92%。为保持对话的新颖性与参与度，聊天机器人的回复从通过波斯语GPT-2与强化学习方法构建的大规模话语数据集中检索，从而最大程度减少人工标注需求。该聊天机器人还配备了名为SAT教师的知识问答模块，用于解答用户关于自我依恋技术原理的疑问。最终，我们设计了跨平台应用程序作为机器人的用户界面。通过一项为期十天、包含52名非临床人群志愿者的真人研究（志愿者与机器人共进行2000余次对话）评估平台效果。结果表明，75%的用户认为平台具有吸引力，72%的用户在交互后感觉改善，74%的用户对SAT教师的表现表示满意。