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

In a classification task, dealing with text snippets and metadata usuallyrequires dealing with multimodal approaches. When those metadata are textual,it is tempting to use them intrinsically with a pre-trained transformer, inorder to leverage the semantic information encoded inside the model. This paperdescribes how to improve a humanitarian classification task by adding thecrisis event type to each tweet to be classified. Based on additionalexperiments of the model weights and behavior, it identifies how the proposedneural network approach is partially over-fitting the particularities of theCrisis Benchmark, to better highlight how the model is still undoubtedlylearning to use and take advantage of the metadata's textual semantics.

How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing