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

Question answer generation using Natural Language Processing models isubiquitous in the world around us. It is used in many use cases such as thebuilding of chat bots, suggestive prompts in google search and also as a way ofnavigating information in banking mobile applications etc. It is highlyrelevant because a frequently asked questions (FAQ) list can only have a finiteamount of questions but a model which can perform question answer generationcould be able to answer completely new questions that are within the scope ofthe data. This helps us to be able to answer new questions accurately as longas it is a relevant question. In commercial applications, it can be used toincrease customer satisfaction and ease of usage. However a lot of data isgenerated by humans so it is susceptible to human error and this can adverselyaffect the model's performance and we are investigating this through our work

A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling