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

Adversarial evaluations of language models typically focus on English alone.In this paper, we performed a multilingual evaluation of Named EntityRecognition (NER) in terms of its robustness to small perturbations in theinput. Our results showed the NER models we explored across three languages(English, German and Hindi) are not very robust to such changes, as indicatedby the fluctuations in the overall F1 score as well as in a more fine-grainedevaluation. With that knowledge, we further explored whether it is possible toimprove the existing NER models using a part of the generated adversarial datasets as augmented training data to train a new NER model or as fine-tuning datato adapt an existing NER model. Our results showed that both these approachesimprove performance on the original as well as adversarial test sets. Whilethere is no significant difference between the two approaches for English,re-training is significantly better than fine-tuning for German and Hindi.

A Multilingual Evaluation of NER Robustness to Adversarial Inputs