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/

Zendy Plus

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

Zendy Tools

Zendy Open

In real-world question-answering (QA) systems, ill-formed questions, such aswrong words, ill word order, and noisy expressions, are common and may preventthe QA systems from understanding and answering them accurately. In order toeliminate the effect of ill-formed questions, we approach the questionrefinement task and propose a unified model, QREFINE, to refine the ill-formedquestions to well-formed question. The basic idea is to learn a Seq2Seq modelto generate a new question from the original one. To improve the quality andretrieval performance of the generated questions, we make two majorimprovements: 1) To better encode the semantics of ill-formed questions, weenrich the representation of questions with character embedding and the recentproposed contextual word embedding such as BERT, besides the traditionalcontext-free word embeddings; 2) To make it capable to generate desiredquestions, we train the model with deep reinforcement learning techniques thatconsiders an appropriate wording of the generation as an immediate reward andthe correlation between generated question and answer as time-delayed long-termrewards. Experimental results on real-world datasets show that the proposedQREFINE method can generate refined questions with more readability but fewermistakes than the original questions provided by users. Moreover, the refinedquestions also significantly improve the accuracy of answer retrieval.

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System