  Stance detection (SD) can be considered a special case of textual entailment
recognition (TER), a generic natural language task. Modelling SD as TER may
offer benefits like more training data and a more general learning scheme. In
this paper, we present an initial empirical analysis of this approach. We apply
it to a difficult but relevant test case where no existing labelled SD dataset
is available, because this is where modelling SD as TER may be especially
helpful. We also leverage measurement knowledge from social sciences to improve
model performance. We discuss our findings and suggest future research
directions.

Computation and Language;Computers and Society;Information Retrieval