The Discussion Tracker Corpus of Collaborative Argumentation
This provides a new dataset for NLP researchers working on argument mining, addressing a gap in multi-party, spoken argumentation, but it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the lack of synchronous, multi-party argumentation datasets in NLP by creating the Discussion Tracker corpus, a transcribed and annotated dataset of 29 spoken discussions from high school English classes, totaling 985 minutes of audio, and provided benchmarks for predicting argument dimensions.
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.