Towards Teachable Conversational Agents
This work addresses the challenge of making machine learning systems more interactive and accessible for non-expert users, though it appears incremental in exploring a specific interface.
The paper tackled the problem of enabling AI agents to learn from human teachers through conversational interactions, finding that teachable conversational agents can reliably learn and identifying key factors for such systems.
The traditional process of building interactive machine learning systems can be viewed as a teacher-learner interaction scenario where the machine-learners are trained by one or more human-teachers. In this work, we explore the idea of using a conversational interface to investigate the interaction between human-teachers and interactive machine-learners. Specifically, we examine whether teachable AI agents can reliably learn from human-teachers through conversational interactions, and how this learning compare with traditional supervised learning algorithms. Results validate the concept of teachable conversational agents and highlight the factors relevant for the development of machine learning systems that intend to learn from conversational interactions.