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

Commonsense and background knowledge is required for a QA model to answermany nontrivial questions. Different from existing work on knowledge-aware QA,we focus on a more challenging task of leveraging external knowledge togenerate answers in natural language for a given question with context.  In this paper, we propose a new neural model, Knowledge-Enriched AnswerGenerator (KEAG), which is able to compose a natural answer by exploiting andaggregating evidence from all four information sources available: question,passage, vocabulary and knowledge. During the process of answer generation,KEAG adaptively determines when to utilize symbolic knowledge and which factfrom the knowledge is useful. This allows the model to exploit externalknowledge that is not explicitly stated in the given text, but that is relevantfor generating an answer. The empirical study on public benchmark of answergeneration demonstrates that KEAG improves answer quality over models withoutknowledge and existing knowledge-aware models, confirming its effectiveness inleveraging knowledge.

Incorporating External Knowledge into Machine Reading for Generative Question Answering