We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
ArgRewrite V.2: an annotated argumentative revisions corpus.
- Authors
Kashefi, Omid; Afrin, Tazin; Dale, Meghan; Olshefski, Christopher; Godley, Amanda; Litman, Diane; Hwa, Rebecca
- 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.
- Subjects
INTELLIGENT tutoring systems; ESSAYS; CORPORA; DRIVERLESS cars; ARTIFICIAL intelligence
- Publication
Language Resources & Evaluation, 2022, Vol 56, Issue 3, p881
- ISSN
1574-020X
- Publication type
Article
- DOI
10.1007/s10579-021-09567-z