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

Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as themajor semi-supervised approaches to improve natural language understanding(NLU) tasks with massive amount of unlabeled data. However, it's unclearwhether they learn similar representations or they can be effectively combined.In this paper, we show that TAPT and ST can be complementary with simple TFSprotocol by following TAPT -> Finetuning -> Self-training (TFS) process.Experimental results show that TFS protocol can effectively utilize unlabeleddata to achieve strong combined gains consistently across six datasets coveringsentiment classification, paraphrase identification, natural languageinference, named entity recognition and dialogue slot classification. Weinvestigate various semi-supervised settings and consistently show that gainsfrom TAPT and ST can be strongly additive by following TFS procedure. We hopethat TFS could serve as an important semi-supervised baseline for future NLPstudies.

Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding