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/

Zendy Plus

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

Zendy Tools

Zendy Open

Transformer-based models for transfer learning have the potential to achievehigh prediction accuracies on text-based supervised learning tasks withrelatively few training data instances. These models are thus likely to benefitsocial scientists that seek to have as accurate as possible text-based measuresbut only have limited resources for annotating training data. To enable socialscientists to leverage these potential benefits for their research, this paperexplains how these methods work, why they might be advantageous, and what theirlimitations are. Additionally, three Transformer-based models for transferlearning, BERT (Devlin et al. 2019), RoBERTa (Liu et al. 2019), and theLongformer (Beltagy et al. 2020), are compared to conventional machine learningalgorithms on three applications. Across all evaluated tasks, textual styles,and training data set sizes, the conventional models are consistentlyoutperformed by transfer learning with Transformers, thereby demonstrating thebenefits these models can bring to text-based social science research.

Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis