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

African languages have far less in-language content available digitally,making it challenging for question answering systems to satisfy the informationneeds of users. Cross-lingual open-retrieval question answering (XOR QA)systems -- those that retrieve answer content from other languages whileserving people in their native language -- offer a means of filling this gap.To this end, we create AfriQA, the first cross-lingual QA dataset with a focuson African languages. AfriQA includes 12,000+ XOR QA examples across 10 Africanlanguages. While previous datasets have focused primarily on languages wherecross-lingual QA augments coverage from the target language, AfriQA focuses onlanguages where cross-lingual answer content is the only high-coverage sourceof answer content. Because of this, we argue that African languages are one ofthe most important and realistic use cases for XOR QA. Our experimentsdemonstrate the poor performance of automatic translation and multilingualretrieval methods. Overall, AfriQA proves challenging for state-of-the-art QAmodels. We hope that the dataset enables the development of more equitable QAtechnology.

AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages