AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining

TL;DR

AutoAM is a novel neural model that performs end-to-end argument mining, capturing complex argument relations beyond tree structures, and outperforms existing methods on public datasets.

Contribution

The paper introduces AutoAM, a universal neural framework with argument component attention for comprehensive argument mining without structural constraints.

Findings

AutoAM outperforms existing methods on multiple metrics.

It effectively captures non-tree argument relations.

The model completes three argument mining subtasks simultaneously.

Abstract

Argument mining is to analyze argument structure and extract important argument information from unstructured text. An argument mining system can help people automatically gain causal and logical information behind the text. As argumentative corpus gradually increases, like more people begin to argue and debate on social media, argument mining from them is becoming increasingly critical. However, argument mining is still a big challenge in natural language tasks due to its difficulty, and relative techniques are not mature. For example, research on non-tree argument mining needs to be done more. Most works just focus on extracting tree structure argument information. Moreover, current methods cannot accurately describe and capture argument relations and do not predict their types. In this paper, we propose a novel neural model called AutoAM to solve these problems. We first introduce the argument component attention mechanism in our model. It can capture the relevant information between argument components, so our model can better perform argument mining. Our model is a universal end-to-end framework, which can analyze argument structure without constraints like tree structure and complete three subtasks of argument mining in one model. The experiment results show that our model outperforms the existing works on several metrics in two public datasets.