  Effective argumentation is essential towards a purposeful conversation with a
satisfactory outcome. For example, persuading someone to reconsider smoking
might involve empathetic, well founded arguments based on facts and expert
opinions about its ill-effects and the consequences on one's family. However,
the automatic generation of high-quality factual arguments can be challenging.
Addressing existing controllability issues can make the recent advances in
computational models for argument generation a potential solution. In this
paper, we introduce ArgU: a neural argument generator capable of producing
factual arguments from input facts and real-world concepts that can be
explicitly controlled for stance and argument structure using Walton's argument
scheme-based control codes. Unfortunately, computational argument generation is
a relatively new field and lacks datasets conducive to training. Hence, we have
compiled and released an annotated corpora of 69,428 arguments spanning six
topics and six argument schemes, making it the largest publicly available
corpus for identifying argument schemes; the paper details our annotation and
dataset creation framework. We further experiment with an argument generation
strategy that establishes an inference strategy by generating an ``argument
template'' before actual argument generation. Our results demonstrate that it
is possible to automatically generate diverse arguments exhibiting different
inference patterns for the same set of facts by using control codes based on
argument schemes and stance.
