Explanatory and Actionable Debugging for Machine Learning: A TableQA Demonstration

Abstract

Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it "actionable" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.