A Joint Identification Approach for Argumentative Writing Revisions

TL;DR

This paper introduces a joint sequence labeling method for identifying argumentative writing revisions, improving accuracy by simultaneously detecting revision locations and types, thus reducing error propagation common in pipeline approaches.

Contribution

It proposes a novel joint sequence labeling approach with mutation-based updates for revision identification, outperforming traditional pipeline methods.

Findings

Better accuracy in revision location extraction

Improved revision type classification performance

Outperforms baseline pipeline approaches

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.