A Multilingual Virtual Guide for Self-Attachment Technique
Alicia Jiayun Law Ruoyu Hu Lisa Alazraki Anandha Gopalan Neophytos Polydorou Abbas Edalat
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.