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Optimizing the phrasing of argumentative text is crucial in higher educationand professional development. However, assessing whether and how the differentclaims in a text should be revised is a hard task, especially for novicewriters. In this work, we explore the main challenges to identifyingargumentative claims in need of specific revisions. By learning fromcollaborative editing behaviors in online debates, we seek to capture implicitrevision patterns in order to develop approaches aimed at guiding writers inhow to further improve their arguments. We systematically compare the abilityof common word embedding models to capture the differences between differentversions of the same text, and we analyze their impact on various types ofwriting issues. To deal with the noisy nature of revision-based corpora, wepropose a new sampling strategy based on revision distance. Opposed toapproaches from prior work, such sampling can be done without employingadditional annotations and judgments. Moreover, we provide evidence that usingcontextual information and domain knowledge can further improve predictionresults. How useful a certain type of context is, depends on the issue theclaim is suffering from, though.

To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support