To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support
This work addresses the challenge of providing automated writing support for higher education and professional development, but it is incremental as it builds on existing methods with a new sampling strategy.
The paper tackles the problem of detecting which argumentative claims need revision to support novice writers, by learning from collaborative editing patterns and comparing word embedding models to capture differences between text versions, showing that contextual information and domain knowledge improve predictions depending on the specific writing issue.
Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should be revised is a hard task, especially for novice writers. In this work, we explore the main challenges to identifying argumentative claims in need of specific revisions. By learning from collaborative editing behaviors in online debates, we seek to capture implicit revision patterns in order to develop approaches aimed at guiding writers in how to further improve their arguments. We systematically compare the ability of common word embedding models to capture the differences between different versions of the same text, and we analyze their impact on various types of writing issues. To deal with the noisy nature of revision-based corpora, we propose a new sampling strategy based on revision distance. Opposed to approaches from prior work, such sampling can be done without employing additional annotations and judgments. Moreover, we provide evidence that using contextual information and domain knowledge can further improve prediction results. How useful a certain type of context is, depends on the issue the claim is suffering from, though.