Aspect-Based Argument Mining

TL;DR

This paper introduces Aspect-Based Argument Mining (ABAM), defining key subtasks, creating an annotated corpus, and evaluating state-of-the-art models to advance argument analysis at the aspect level.

Contribution

It presents the first annotated corpus for aspect-based argument mining and evaluates models on subtasks of aspect term extraction and nested segmentation.

Findings

State-of-the-art models perform well on ATE and NS subtasks.

Annotated corpus is publicly available for further research.

ABAM enables finer-grained argument analysis and downstream applications.

Abstract

Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.