Collaborative Knowledge Infusion and Efficient Learning for Robust Stance Detection in Low-Resource Settings
A collaborative knowledge infusion approach that leverages target background knowledge from multiple sources and employs efficient parameter learning techniques to address the challenges of low-resource stance detection tasks.
Dynamic Experienced Expert Modeling for Stance Detection
Leveraging dynamically generated and filtered experienced experts can substantially improve the performance of large language models on stance detection tasks.
Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
A novel collaborative stance detection framework (CoSD) that leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection.
Classifying Political Beliefs in Text: A Practical Guide to Stance Detection
Stance detection is the identification of an author's beliefs about a subject from a text sample. This paper presents a precise definition of stance detection as an entailment classification task, provides a generalized framework for the task, and demonstrates three distinct approaches for performing stance detection: supervised classification, zero-shot classification with NLI classifiers, and in-context learning.
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