  Current conversational AI systems aim to understand a set of pre-designed
requests and execute related actions, which limits them to evolve naturally and
adapt based on human interactions. Motivated by how children learn their first
language interacting with adults, this paper describes a new Teachable AI
system that is capable of learning new language nuggets called concepts,
directly from end users using live interactive teaching sessions. The proposed
setup uses three models to: a) Identify gaps in understanding automatically
during live conversational interactions, b) Learn the respective
interpretations of such unknown concepts from live interactions with users, and
c) Manage a classroom sub-dialogue specifically tailored for interactive
teaching sessions. We propose state-of-the-art transformer based neural
architectures of models, fine-tuned on top of pre-trained models, and show
accuracy improvements on the respective components. We demonstrate that this
method is very promising in leading way to build more adaptive and personalized
language understanding models.
