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Register a new userMATLAB based language for generating randomized multiple choice questions
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Added by
Nurulla Azamov Dr
Publication date
2015
fields
Informatics Engineering
and research's language is
English
Authors
Nurulla Azamov
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In this work we describe a simple MATLAB based language which allows to create randomized multiple choice questions with minimal effort. This language has been successfully tested at Flinders University by the author in a number of mathematics topics including Numerical Analysis, Abstract Algebra and Partial Differential Equations. The open source code of Spike is available at: https://github.com/NurullaAzamov/Spike. Enquiries about Spike should be sent to [email protected]
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We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.
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We present a novel approach to answer the Chinese elementary school Social Study Multiple Choice questions. Although BERT has demonstrated excellent performance on Reading Comprehension tasks, it is found not good at handling some specific types of questions, such as Negation, All-of-the-above, and None-of-the-above. We thus propose a novel framework to cascade BERT with a Pre-Processor and an Answer-Selector modules to tackle the above challenges. Experimental results show the proposed approach effectively improves the performance of BERT, and thus demonstrate the feasibility of supplementing BERT with additional modules.
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