Parsing Argumentation Structures in Persuasive Essays

TL;DR

This paper introduces a new joint model for parsing argumentation structures in persuasive essays, improving accuracy and providing a new annotated corpus for future research.

Contribution

A novel joint model for argument component detection and structure parsing, along with a publicly available annotated corpus and guidelines.

Findings

Model significantly outperforms heuristic baselines

Proposed annotation scheme achieves high inter-annotator agreement

New corpus facilitates future computational argumentation research

Abstract

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.