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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Fine-tuning pre-trained language models for downstream tasks has become anorm for NLP. Recently it is found that intermediate training based onhigh-level inference tasks such as Question Answering (QA) can improve theperformance of some language models for target tasks. However it is not clearif intermediate training generally benefits various language models. In thispaper, using the SQuAD-2.0 QA task for intermediate training for target textclassification tasks, we experimented on eight tasks for single-sequenceclassification and eight tasks for sequence-pair classification using two baseand two compact language models. Our experiments show that QA-basedintermediate training generates varying transfer performance across differentlanguage models, except for similar QA tasks.

Does QA-based intermediate training help fine-tuning language models for text classification?