Annotation and Classification of Argumentative Writing Revisions

Abstract

Prior work on revision identification typically uses a pipeline method: revision extraction is first conducted to identify the locations of revisions and revision classification is then conducted on the identified revisions. Such a setting propagates the errors of the revision extraction step to the revision classification step. This paper proposes an approach that identifies the revision location and the revision type jointly to solve the issue of error propagation. It utilizes a sequence representation of revisions and conducts sequence labeling for revision identification. A mutation-based approach is utilized to update identification sequences. Results demonstrate that our proposed approach yields better performance on both revision location extraction and revision type classification compared to a pipeline baseline. 1 The categories are defined according to Toulmin's argumentation model (Toulmin, 2003).2 Surface include changes such as spelling correction and sentence reorderings that do not change a paper's content.

Identifying Editor Roles in Argumentative Writing from Student Revision Histories

“…Our work takes advantage of several corpora of multiple drafts of argumentative essays written by both high-school and college students [12,11], where all data has been annotated for revision using the framework of [12]. We divide our data into a Modeling Corpus (185 paired drafts, 3245 revisions) and an Evaluation Corpus (107 paired draft, 2045 revisions), based on whether expert grades are available before (Score1) and after (Score2) essay revision.…”

Annotation and Classification of Sentence-level Revision Improvement

“…Prior NLP revision analysis work has developed methods for identifying pairs of original and revised textual units in both Wikipedia articles and student essays, as well as for classifying such pairs with respect to schemas of coarse (e.g., syntactic versus semantic) and fine-grained (e.g., lexical vs. grammatical syntactic changes) revision purposes (Bronner and Monz, 2012;Daxenberger and Gurevych, 2012;Zhang and Litman, 2015;Yang et al, 2017). For example, the ArgRewrite corpus (Zhang et al, 2017) was introduced with the goal to facilitate argumentative revision analysis and automatic revision purpose classification.…”

“…We adapt many features from prior studies predicting revision purposes (Adler et al, 2011;Javanmardi et al, 2011;Bronner and Monz, 2012;Daxenberger and Gurevych, 2013;Zhang and Litman, 2015;Remse et al, 2016) as well as introduce new features tailored to predicting improvement.…”

Automatic argument assessment of final project reports of computer engineering students

“…For argumentative writing revision, we found the methodology presented in [35] to identify jointly the location and type of revisions, using two versions of essays. They recognized different types of changes in the text such as surface or reasoning.…”