In this work, we propose a computational framework that leverages existing
out-of-language data to create a conversational agent for the delivery of
Self-Attachment Technique (SAT) in Mandarin. Our framework does not require
large-scale human translations, yet it achieves a comparable performance whilst
also maintaining safety and reliability. We propose two different methods of
augmenting available response data through empathetic rewriting. We evaluate
our chatbot against a previous, English-only SAT chatbot through non-clinical
human trials (N=42), each lasting five days, and quantitatively show that we
are able to attain a comparable level of performance to the English SAT
chatbot. We provide qualitative analysis on the limitations of our study and
suggestions with the aim of guiding future improvements.

我们提出了一个计算框架，利用现有的非语言数据创建一个普通话的对话系统，用于自我附着技术（Self-Attachment Technique，SAT）的交付。我们的框架不需要大规模人工翻译，但在保证安全和可靠性的同时，也能达到可比较的性能。我们提出了两种通过共情式重写来增强可用的回应数据的方法。通过 42 个非临床人类试验（每个持续五天），我们对我们的对话机器人与之前的仅英语 SAT 对话机器人进行了定量评估，并定量证明我们能够达到与英语 SAT 对话机器人相当的性能水平。我们对研究的限制进行了定性分析，并提出了建议，旨在指导未来的改进。