# The Discussion Tracker Corpus of Collaborative Argumentation

arXiv: https://arxiv.org/abs/2005.11344 · HTML: https://mlsanity.com/paper/2005.11344/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 5 of 5)

- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [Contextual Argument Component Classification for Class Discussions](https://mlsanity.com/paper/2102.10290/) (2021)
- [Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms](https://mlsanity.com/paper/2102.10293/) (2021)
- [Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion](https://mlsanity.com/paper/2306.14918/) (2023)
- [Automatic Analysis of Collaboration Through Human Conversational Data Resources: A Review](https://mlsanity.com/paper/2603.19292/) (2026)

## Abstract (arXiv)

Although Natural Language Processing (NLP) research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of synchronous, multi-party argumentation are available. The Discussion Tracker corpus, collected in American high school English classes, is an annotated dataset of transcripts of spoken, multi-party argumentation. The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio. The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others' ideas). In addition to providing descriptive statistics on the corpus, we provide performance benchmarks and associated code for predicting each dimension separately, illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.