  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.
