Contextual Argument Component Classification for Class Discussions

TL;DR

This paper investigates how local discourse and speaker context can enhance argument component classification in multi-party classroom discussions, demonstrating that incorporating these contexts improves model performance depending on their size and position.

Contribution

It introduces a computational model that effectively integrates local discourse and speaker context for argument component classification in classroom discussions.

Findings

Both context types improve classification accuracy.

Performance gains depend on context size and position.

Contextual information enhances argument mining in educational settings.

Abstract

Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.