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Generating and Ranking Distractors for Multiple-Choice Questions in Portuguese

Authors Hugo Gonçalo Oliveira , Igor Caetano, Renato Matos, Hugo Amaro

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Author Details

Hugo Gonçalo Oliveira
  • Center of Informatics and Systems, University of Coimbra, Portugal
  • Department of Informatics Engineering, University of Coimbra, Portugal
Igor Caetano
  • Instituto Pedro Nunes, Coimbra, Portugal
  • Department of Informatics Engineering, University of Coimbra, Portugal
Renato Matos
  • Center of Informatics and Systems, University of Coimbra, Portugal
  • Department of Informatics Engineering, University of Coimbra, Portugal
Hugo Amaro
  • Instituto Pedro Nunes, LIS, Coimbra, Portugal

Funding

This work was funded by: project SmartEDU (CENTRO-01-0247-FEDER-072620), co-financed by FEDER, through PT2020, and by the Regional Operational Programme Centro 2020; and through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by the European Social Fund, through the Regional Operational Program Centro 2020.

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Hugo Gonçalo Oliveira, Igor Caetano, Renato Matos, and Hugo Amaro. Generating and Ranking Distractors for Multiple-Choice Questions in Portuguese. In 12th Symposium on Languages, Applications and Technologies (SLATE 2023). Open Access Series in Informatics (OASIcs), Volume 113, pp. 4:1-4:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/OASIcs.SLATE.2023.4

Abstract

In the process of multiple-choice question generation, different methods are often considered for distractor acquisition, as an attempt to cover as many questions as possible. Some, however, result in many candidate distractors of variable quality, while only three or four are necessary. We implement some distractor generation methods for Portuguese and propose their combination and ranking with language models. Experimentation results confirm that this increases both coverage and suitability of the selected distractors.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • Multiple-Choice Questions
  • Distractor Generation
  • Language Models

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References

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