  Stance detection determines whether the author of a piece of text is in favor
of, against, or neutral towards a specified target, and can be used to gain
valuable insights into social media. The ubiquitous indirect referral of
targets makes this task challenging, as it requires computational solutions to
model semantic features and infer the corresponding implications from a literal
statement. Moreover, the limited amount of available training data leads to
subpar performance in out-of-domain and cross-target scenarios, as data-driven
approaches are prone to rely on superficial and domain-specific features. In
this work, we decompose the stance detection task from a linguistic
perspective, and investigate key components and inference paths in this task.
The stance triangle is a generic linguistic framework previously proposed to
describe the fundamental ways people express their stance. We further expand it
by characterizing the relationship between explicit and implicit objects. We
then use the framework to extend one single training corpus with additional
annotation. Experimental results show that strategically-enriched data can
significantly improve the performance on out-of-domain and cross-target
evaluation.
