ArgRewrite V.2: an Annotated Argumentative Revisions Corpus

TL;DR

ArgRewrite V.2 is a comprehensive annotated corpus of argumentative essay revisions, designed to advance NLP research and applications in understanding and predicting human revision behavior.

Contribution

This work introduces ArgRewrite V.2, a detailed revision corpus with multi-level annotations and meta-data, facilitating research in revision analysis and automatic prediction.

Findings

Demonstrated potential for automatic revision purpose prediction

Provided a valuable resource for NLP research on writing revisions

Enhanced understanding of revision strategies in argumentative writing

Abstract

Analyzing how humans revise their writings is an interesting research question, not only from an educational perspective but also in terms of artificial intelligence. Better understanding of this process could facilitate many NLP applications, from intelligent tutoring systems to supportive and collaborative writing environments. Developing these applications, however, requires revision corpora, which are not widely available. In this work, we present ArgRewrite V.2, a corpus of annotated argumentative revisions, collected from two cycles of revisions to argumentative essays about self-driving cars. Annotations are provided at different levels of purpose granularity (coarse and fine) and scope (sentential and subsentential). In addition, the corpus includes the revision goal given to each writer, essay scores, annotation verification, pre- and post-study surveys collected from participants as meta-data. The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis. We demonstrate some potential applications of ArgRewrite V.2 in the development of automatic revision purpose predictors, as a training source and benchmark.