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