To Revise or Not to Revise: Learning to Detect Improvable Claims for
  Argumentative Writing Support
Gabriella Skitalinskaya Henning Wachsmuth
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.