Reinforcement Learning Based Argument Component Detection

TL;DR

This paper introduces a reinforcement learning approach for argument component detection that effectively incorporates historical annotations, significantly improving classification accuracy over existing methods.

Contribution

The paper proposes a novel RL-based ACD method that leverages historical annotations, outperforming traditional RL and state-of-the-art supervised algorithms.

Findings

HAs-augmented RL improves accuracy by up to 17.85%.

The method outperforms state-of-the-art algorithms by up to 11.94%.

Reinforcement learning effectively utilizes historical annotations in ACD.

Abstract

Argument component detection (ACD) is an important sub-task in argumentation mining. ACD aims at detecting and classifying different argument components in natural language texts. Historical annotations (HAs) are important features the human annotators consider when they manually perform the ACD task. However, HAs are largely ignored by existing automatic ACD techniques. Reinforcement learning (RL) has proven to be an effective method for using HAs in some natural language processing tasks. In this work, we propose a RL-based ACD technique, and evaluate its performance on two well-annotated corpora. Results suggest that, in terms of classification accuracy, HAs-augmented RL outperforms plain RL by at most 17.85%, and outperforms the state-of-the-art supervised learning algorithm by at most 11.94%.