  Prior studies of zero-shot stance detection identify the attitude of texts
towards unseen topics occurring in the same document corpus. Such task
formulation has three limitations: (i) Single domain/dataset. A system is
optimized on a particular dataset from a single domain; therefore, the
resulting system cannot work well on other datasets; (ii) the model is
evaluated on a limited number of unseen topics; (iii) it is assumed that part
of the topics has rich annotations, which might be impossible in real-world
applications. These drawbacks will lead to an impractical stance detection
system that fails to generalize to open domains and open-form topics. This work
defines OpenStance: open-domain zero-shot stance detection, aiming to handle
stance detection in an open world with neither domain constraints nor
topic-specific annotations. The key challenge of OpenStance lies in the
open-domain generalization: learning a system with fully unspecific supervision
but capable of generalizing to any dataset. To solve OpenStance, we propose to
combine indirect supervision, from textual entailment datasets, and weak
supervision, from data generated automatically by pre-trained Language Models.
Our single system, without any topic-specific supervision, outperforms the
supervised method on three popular datasets. To our knowledge, this is the
first work that studies stance detection under the open-domain zero-shot
setting. All data and code are publicly released.
