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

Multiword expressions are a key ingredient for developing large-scale andlinguistically sound natural language processing technology. This paperdescribes our improvements in automatically identifying Romanian multiwordexpressions on the corpus released for the PARSEME v1.2 shared task. Ourapproach assumes a multilingual perspective based on the recently introducedlateral inhibition layer and adversarial training to boost the performance ofthe employed multilingual language models. With the help of these two methods,we improve the F1-score of XLM-RoBERTa by approximately 2.7% on unseenmultiword expressions, the main task of the PARSEME 1.2 edition. In addition,our results can be considered SOTA performance, as they outperform the previousresults on Romanian obtained by the participants in this competition.

Romanian Multiword Expression Detection Using Multilingual Adversarial Training and Lateral Inhibition