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

Relation extraction (RE) tasks show promising performance in extractingrelations from two entities mentioned in sentences, given sufficientannotations available during training. Such annotations would belabor-intensive to obtain in practice. Existing work adopts data augmentationtechniques to generate pseudo-annotated sentences beyond limited annotations.These techniques neither preserve the semantic consistency of the originalsentences when rule-based augmentations are adopted, nor preserve the syntaxstructure of sentences when expressing relations using seq2seq models,resulting in less diverse augmentations. In this work, we propose a dedicatedaugmentation technique for relational texts, named GDA, which uses twocomplementary modules to preserve both semantic consistency and syntaxstructures. We adopt a generative formulation and design a multi-taskingsolution to achieve synergies. Furthermore, GDA adopts entity hints as theprior knowledge of the generative model to augment diverse sentences.Experimental results in three datasets under a low-resource setting showed thatGDA could bring {\em 2.0\%} F1 improvements compared with no augmentationtechnique. Source code and data are available.

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks