# Dissecting Content and Context in Argumentative Relation Analysis

## TL;DR

This paper reveals that systems often rely heavily on contextual cues rather than the actual argumentative content, and proposes a method to focus on content for more robust relation analysis.

## Contribution

It introduces a technique to separate argumentative content from context, improving robustness and cross-document relation prediction in argument analysis systems.

## Key findings

- Context-only models can outperform content-based models when content is masked.
- Separating content from context makes systems more robust against manipulation.
- Content-focused models are better for cross-document argumentative relation prediction.

## Abstract

When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.). We show that this dependency is much stronger than previously assumed. In fact, we show that by completely masking the EAU text spans and only feeding information from their context, a competitive system may function even better. We argue that an argument analysis system that relies more on discourse context than the argument's content is unsafe, since it can easily be tricked. To alleviate this issue, we separate argumentative units from their context such that the system is forced to model and rely on an EAU's content. We show that the resulting classification system is more robust, and argue that such models are better suited for predicting argumentative relations across documents.