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

To audit the robustness of named entity recognition (NER) models, we proposeRockNER, a simple yet effective method to create natural adversarial examples.Specifically, at the entity level, we replace target entities with otherentities of the same semantic class in Wikidata; at the context level, we usepre-trained language models (e.g., BERT) to generate word substitutions.Together, the two levels of attack produce natural adversarial examples thatresult in a shifted distribution from the training data on which our targetmodels have been trained. We apply the proposed method to the OntoNotes datasetand create a new benchmark named OntoRock for evaluating the robustness ofexisting NER models via a systematic evaluation protocol. Our experiments andanalysis reveal that even the best model has a significant performance drop,and these models seem to memorize in-domain entity patterns instead ofreasoning from the context. Our work also studies the effects of a few simpledata augmentation methods to improve the robustness of NER models.

RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models