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

In this paper, we investigate the following two limitations for the existingdistractor generation (DG) methods. First, the quality of the existing DGmethods are still far from practical use. There is still room for DG qualityimprovement. Second, the existing DG designs are mainly for single distractorgeneration. However, for practical MCQ preparation, multiple distractors aredesired. Aiming at these goals, in this paper, we present a new distractorgeneration scheme with multi-tasking and negative answer training strategiesfor effectively generating \textit{multiple} distractors. The experimentalresults show that (1) our model advances the state-of-the-art result from 28.65to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverseand show strong distracting power for multiple choice question.

A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies