STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive
  Learning and Counterfactual Generation
Nayoung Kim David Mosallanezhad Lu Cheng Michelle V. Mancenido Huan Liu
Stance detection is the process of inferring a person's position or
standpoint on a specific issue to deduce prevailing perceptions toward topics
of general or controversial interest, such as health policies during the
COVID-19 pandemic. Existing models for stance detection are trained to perform
well for a single domain (e.g., COVID-19) and a specific target topic (e.g.,
masking protocols), but are generally ineffectual in other domains or targets
due to distributional shifts in the data. However, constructing
high-performing, domain-specific stance detection models requires an extensive
corpus of labeled data relevant to the targeted domain, yet such datasets are
not readily available. This poses a challenge as the process of annotating data
is costly and time-consuming. To address these challenges, we introduce a novel
stance detection model coined domain-adaptive Cross-target STANCE detection via
Contrastive learning and Counterfactual generation (STANCE-C3) that uses
counterfactual data augmentation to enhance domain-adaptive training by
enriching the target domain dataset during the training process and requiring
significantly less information from the new domain. We also propose a modified
self-supervised contrastive learning as a component of STANCE-C3 to prevent
overfitting for the existing domain and target and enable cross-target stance
detection. Through experiments on various datasets, we show that STANCE-C3
shows performance improvement over existing state-of-the-art methods.