English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data collection from manual labeling provides domain-specific andtask-aligned supervision for data-driven approaches, and a critical mass ofwell-annotated resources is required to achieve reasonable performance innatural language processing tasks. However, manual annotations are oftenchallenging to scale up in terms of time and budget, especially when domainknowledge, capturing subtle semantic features, and reasoning steps are needed.In this paper, we investigate the efficacy of leveraging large language modelson automated labeling for computational stance detection. We empiricallyobserve that while large language models show strong potential as analternative to human annotators, their sensitivity to task-specificinstructions and their intrinsic biases pose intriguing yet unique challengesin machine annotation. We introduce a multi-label and multi-target samplingstrategy to optimize the annotation quality. Experimental results on thebenchmark stance detection corpora show that our method can significantlyimprove performance and learning efficacy.

Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection