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

We study open-domain question answering with structured, unstructured andsemi-structured knowledge sources, including text, tables, lists and knowledgebases. Departing from prior work, we propose a unifying approach thathomogenizes all sources by reducing them to text and applies theretriever-reader model which has so far been limited to text sources only. Ourapproach greatly improves the results on knowledge-base QA tasks by 11 points,compared to latest graph-based methods. More importantly, we demonstrate thatour unified knowledge (UniK-QA) model is a simple and yet effective way tocombine heterogeneous sources of knowledge, advancing the state-of-the-artresults on two popular question answering benchmarks, NaturalQuestions andWebQuestions, by 3.5 and 2.6 points, respectively.  The code of UniK-QA is available at:https://github.com/facebookresearch/UniK-QA.

UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering