A Framework for Interactive Knowledge-Aided Machine Teaching
This work addresses the problem of inefficient and poorly designed MT systems for researchers and practitioners, though it appears incremental as it builds on existing MT concepts.
The authors tackled the lack of a general framework for designing Machine Teaching (MT) systems by proposing a framework with three components—teaching interface, machine learner, and knowledge base—and demonstrated its application to text classification, showing reductions in human teaching time and machine learner error rate.
Machine Teaching (MT) is an interactive process where humans train a machine learning model by playing the role of a teacher. The process of designing an MT system involves decisions that can impact both efficiency of human teachers and performance of machine learners. Previous research has proposed and evaluated specific MT systems but there is limited discussion on a general framework for designing them. We propose a framework for designing MT systems and also detail a system for the text classification problem as a specific instance. Our framework focuses on three components i.e. teaching interface, machine learner, and knowledge base; and their relations describe how each component can benefit the others. Our preliminary experiments show how MT systems can reduce both human teaching time and machine learner error rate.