Modeling stance in student essays

Abstract

Essay stance classification, the task of determining how much an essay's author agrees with a given proposition, is an important yet under-investigated subtask in understanding an argumentative essay's overall content. We introduce a new corpus of argumentative student essays annotated with stance information and propose a computational model for automatically predicting essay stance. In an evaluation on 826 essays, our approach significantly outperforms four baselines, one of which relies on features previously developed specifically for stance classification in student essays, yielding relative error reductions of at least 11.3% and 5.3%, in micro and macro F-score, respectively.