  In real-world debates, the most common way to counter an argument is to
reason against its main point, that is, its conclusion. Existing work on the
automatic generation of natural language counter-arguments does not address the
relation to the conclusion, possibly because many arguments leave their
conclusion implicit. In this paper, we hypothesize that the key to effective
counter-argument generation is to explicitly model the argument's conclusion
and to ensure that the stance of the generated counter is opposite to that
conclusion. In particular, we propose a multitask approach that jointly learns
to generate both the conclusion and the counter of an input argument. The
approach employs a stance-based ranking component that selects the counter from
a diverse set of generated candidates whose stance best opposes the generated
conclusion. In both automatic and manual evaluation, we provide evidence that
our approach generates more relevant and stance-adhering counters than strong
baselines.

Computation and Language