  Peer-review plays a critical role in the scientific writing and publication
ecosystem. To assess the efficiency and efficacy of the reviewing process, one
essential element is to understand and evaluate the reviews themselves. In this
work, we study the content and structure of peer reviews under the argument
mining framework, through automatically detecting (1) argumentative
propositions put forward by reviewers, and (2) their types (e.g., evaluating
the work or making suggestions for improvement). We first collect 14.2K reviews
from major machine learning and natural language processing venues. 400 reviews
are annotated with 10,386 propositions and corresponding types of Evaluation,
Request, Fact, Reference, or Quote. We then train state-of-the-art proposition
segmentation and classification models on the data to evaluate their utilities
and identify new challenges for this new domain, motivating future directions
for argument mining. Further experiments show that proposition usage varies
across venues in amount, type, and topic.
