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

For the field of education, being able to generate semantically correct andeducationally relevant multiple choice questions (MCQs) could have a largeimpact. While question generation itself is an active research topic,generating distractors (the incorrect multiple choice options) receives muchless attention. A missed opportunity, since there is still a lot of room forimprovement in this area. In this work, we train a GPT-2 language model togenerate three distractors for a given question and text context, using theRACE dataset. Next, we train a BERT language model to answer MCQs, and use thismodel as a filter, to select only questions that can be answered and thereforepresumably make sense. To evaluate our work, we start by using text generationmetrics, which show that our model outperforms earlier work on distractorgeneration (DG) and achieves state-of-the-art performance. Also, by calculatingthe question answering ability, we show that larger base models lead to betterperformance. Moreover, we conducted a human evaluation study, which confirmedthe quality of the generated questions, but showed no statistically significanteffect of the QA filter.

Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering