Abstract
Competitive debaters often find themselves
facing a challenging task – how to debate a
topic they know very little about, with only
minutes to prepare, and without access to
books or the Internet? What they often do is
rely on ”first principles”, commonplace arguments which are relevant to many topics, and
which they have refined in past debates. In this
work we aim to explicitly define a taxonomy
of such principled recurring arguments, and,
given a controversial topic, to automatically
identify which of these arguments are relevant
to the topic. As far as we know, this is the first
time that this approach to argument invention
is formalized and made explicit in the context
of NLP. The main goal of this work is to show
that it is possible to define such a taxonomy.
While the taxonomy suggested here should be
thought of as a ”first attempt” it is nonetheless
coherent, covers well the relevant topics and
coincides with what professional debaters actually argue in their speeches, and facilitates
automatic argument invention for new topics