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

Dense retrieval models, which aim at retrieving the most relevant documentfor an input query on a dense representation space, have gained considerableattention for their remarkable success. Yet, dense models require a vast amountof labeled training data for notable performance, whereas it is oftenchallenging to acquire query-document pairs annotated by humans. To tackle thisproblem, we propose a simple but effective Document Augmentation for denseRetrieval (DAR) framework, which augments the representations of documents withtheir interpolation and perturbation. We validate the performance of DAR onretrieval tasks with two benchmark datasets, showing that the proposed DARsignificantly outperforms relevant baselines on the dense retrieval of both thelabeled and unlabeled documents.

Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation