End-to-End Argumentation Mining in Student Essays

Abstract

Understanding the argumentative structure of a persuasive essay involves addressing two challenging tasks: identifying the components of the essay’s argument and identifying the relations that occur between them. We examine the under-investigated task of end-to-end argument mining in persuasive student essays, where we (1) present the first results on end-to-end argument mining in student essays using a pipeline approach; (2) address error propagation inherent in the pipeline approach by performing joint inference over the outputs of the tasks in an Integer Linear Programming (ILP) framework; and (3) propose a novel objective function that enables F-score to be maximized directly by an ILP solver. We evaluate our joint-inference approach with our novel objective function on a publicly-available corpus of 90 essays, where it yields an 18.5% relative error reduction in F-score over the pipeline system.