ArgRewrite V.2: an Annotated Argumentative Revisions Corpus
This provides a resource for researchers and developers working on NLP applications like intelligent tutoring systems, though it is incremental as an expanded version of an existing corpus.
They tackled the lack of revision corpora for NLP applications by presenting ArgRewrite V.2, a corpus of annotated argumentative revisions from essays, which includes multi-level annotations and meta-data, and demonstrated its use for developing automatic revision purpose predictors.
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.