  This paper presents a novel teachable conversation interaction system that is
capable of learning users preferences from cold start by gradually adapting to
personal preferences. In particular, the TAI system is able to automatically
identify and label user preference in live interactions, manage dialogue flows
for interactive teaching sessions, and reuse learned preference for preference
elicitation. We develop the TAI system by leveraging BERT encoder models to
encode both dialogue and relevant context information, and build action
prediction (AP), argument filling (AF) and named entity recognition (NER)
models to understand the teaching session. We adopt a seeker-provider
interaction loop mechanism to generate diverse dialogues from cold-start. TAI
is capable of learning user preference, which achieves 0.9122 turn level
accuracy on out-of-sample dataset, and has been successfully adopted in
production.
