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

In this paper, we describe our approach in the shared task: COVID-19 eventextraction from Twitter. The objective of this task is to extract answers fromCOVID-related tweets to a set of predefined slot-filling questions. Ourapproach treats the event extraction task as a question answering task byleveraging the transformer-based T5 text-to-text model.  According to the official evaluation scores returned, namely F1, oursubmitted run achieves competitive performance compared to other participatingruns (Top 3). However, we argue that this evaluation may underestimate theactual performance of runs based on text-generation. Although some such runsmay answer the slot questions well, they may not be an exact string match forthe gold standard answers. To measure the extent of this underestimation, weadopt a simple exact-answer transformation method aiming at converting thewell-answered predictions to exactly-matched predictions. The results show thatafter this transformation our run overall reaches the same level of performanceas the best participating run and state-of-the-art F1 scores in three of fiveCOVID-related events. Our code is publicly available to aid reproducibility

UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media