# Contextual Argument Component Classification for Class Discussions

arXiv: https://arxiv.org/abs/2102.10290 · HTML: https://mlsanity.com/paper/2102.10290/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Dissecting Content and Context in Argumentative Relation Analysis](https://mlsanity.com/paper/1906.03338/) (2019)
- [Argument Component Classification for Classroom Discussions](https://mlsanity.com/paper/1909.03022/) (2019)
- [The Discussion Tracker Corpus of Collaborative Argumentation](https://mlsanity.com/paper/2005.11344/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [Analyzing Large Language Models for Classroom Discussion Assessment](https://mlsanity.com/paper/2406.08680/) (2024)
- [Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment](https://mlsanity.com/paper/2609.26338/) (2026)

## Abstract (arXiv)

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.