  Systems for automatic argument generation and debate require the ability to
(1) determine the stance of any claims employed in the argument and (2) assess
the specificity of each claim relative to the argument context. Existing work
on understanding claim specificity and stance, however, has been limited to the
study of argumentative structures that are relatively shallow, most often
consisting of a single claim that directly supports or opposes the argument
thesis. In this paper, we tackle these tasks in the context of complex
arguments on a diverse set of topics. In particular, our dataset consists of
manually curated argument trees for 741 controversial topics covering 95,312
unique claims; lines of argument are generally of depth 2 to 6. We find that as
the distance between a pair of claims increases along the argument path,
determining the relative specificity of a pair of claims becomes easier and
determining their relative stance becomes harder.

Computation and Language;Artificial Intelligence