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

Generating high quality question-answer pairs is a hard but meaningful task.Although previous works have achieved great results on answer-aware questiongeneration, it is difficult to apply them into practical application in theeducation field. This paper for the first time addresses the question-answerpair generation task on the real-world examination data, and proposes a newunified framework on RACE. To capture the important information of the inputpassage we first automatically generate(rather than extracting) keyphrases,thus this task is reduced to keyphrase-question-answer triplet jointgeneration. Accordingly, we propose a multi-agent communication model togenerate and optimize the question and keyphrases iteratively, and then applythe generated question and keyphrases to guide the generation of answers. Toestablish a solid benchmark, we build our model on the strong generativepre-training model. Experimental results show that our model makes greatbreakthroughs in the question-answer pair generation task. Moreover, we make acomprehensive analysis on our model, suggesting new directions for thischallenging task.

Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data