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Generation and Evaluation of Multiple-choice Reading Comprehension Questions for Swedish
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0001-7327-3059
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-2600-7668
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Multiple-choice questions (MCQs) provide a widely used means of assessing reading comprehension. The automatic generation of such MCQs is a challenging language-technological problem that also has interesting educational applications. This article presents several methods for automatically producing reading comprehension questions MCQs from Swedish text. Unlike previous approaches, we construct models to generate the whole MCQ in one go, rather than using a pipeline architecture. Furthermore, we propose a two-stage method for evaluating the quality of the generated MCQs, first evaluating on carefully designed single-sentence texts, and then on texts from the SFI national exams. An extensive evaluation of the MCQ-generating capabilities of 12 different models, using this two-stage scheme, reveals that GPT-based models surpass smaller models that have been fine-tuned using small-scale datasets on this specific problem.

Keywords [en]
Natural Language Generation, Natural Language Processing, Question Generation, Distractor Generation, Reading Comprehension, Multiple choice Questions
National Category
Natural Language Processing
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-329400OAI: oai:DiVA.org:kth-329400DiVA, id: diva2:1771278
Funder
Vinnova, 2019-02997
Note

QC 20230627

Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2025-02-07Bibliographically approved
In thesis
1. Ask and distract: Data-driven methods for the automatic generation of multiple-choice reading comprehension questions from Swedish texts
Open this publication in new window or tab >>Ask and distract: Data-driven methods for the automatic generation of multiple-choice reading comprehension questions from Swedish texts
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Fråga och distrahera : Datadrivna metoder för automatisk generering av flervalsfrågor för att bedöma läsförståelse av svenska
Abstract [en]

Multiple choice questions (MCQs) are widely used for summative assessment in many different subjects. The tasks in this format are particularly appealing because they can be graded swiftly and automatically. However, the process of creating MCQs is far from swift or automatic and requires a lot of expertise both in the specific subject and also in test construction.

This thesis focuses on exploring methods for the automatic MCQ generation for assessing the reading comprehension abilities of second-language learners of Swedish. We lay the foundations for the MCQ generation research for Swedish by collecting two datasets of reading comprehension MCQs, and designing and developing methods for generating the whole MCQs or its parts. An important contribution is the methods (which were designed and applied in practice) for the automatic and human evaluation of the generated MCQs.

The best currently available method (as of June 2023) for generating MCQs for assessing reading comprehension in Swedish is ChatGPT (although still only around 60% of generated MCQs were judged acceptable). However, ChatGPT is neither open-source, nor free. The best open-source and free-to-use method is the fine-tuned version of SweCTRL-Mini, a foundational model developed as a part of this thesis. Nevertheless, all explored methods are far from being useful in practice but the reported results provide a good starting point for future research.

Abstract [sv]

Flervalsfrågor används ofta för summativ bedömning i många olika ämnen. Flervalsfrågor är tilltalande eftersom de kan bedömas snabbt och automatiskt. Att skapa flervalsfrågor manuellt går dock långt ifrån snabbt, utan är en process som kräver mycket expertis inom det specifika ämnet och även inom provkonstruktion.

Denna avhandling fokuserar på att utforska metoder för automatisk generering av flervalsfrågor för bedömning av läsförståelse hos andraspråksinlärare av svenska. Vi lägger grunden för forskning om generering av flervalsfrågor för svenska genom att samla in två datamängder bestående av flervalsfrågor som testar just läsförståelse, och genom att utforma och utveckla metoder för att generera hela eller delar av flervalsfrågor. Ett viktigt bidrag är de metoder för automatisk och mänsklig utvärdering av genererade flervalsfrågor som har utvecklats och tillämpats i praktiken.

Den bästa för närvarande tillgängliga metoden (i juni 2023) för att generera flervalsfrågor som testar läsförståelse på svenska är ChatGPT (dock bedömdes endast cirka 60% av de genererade flervalsfrågorna som acceptabla). ChatGPT har dock varken öppen källkod eller är gratis. Den bästa metoden med öppen källkod som är också gratis är den finjusterade versionen av SweCTRL-Mini, en “foundational model” som utvecklats som en del av denna avhandling. Alla utforskade metoder är dock långt ifrån användbara i praktiken, men de rapporterade resultaten ger en bra utgångspunkt för framtida forskning.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2023. p. viii, 67
Series
TRITA-EECS-AVL ; 2023:56
Keywords
multiple choice questions, question generation, distractor generation, reading comprehension, second-language learners, L2 learning, Natural Language Generation, flervalsfrågor, frågegenerering, distraktorsgenerering, läsförståelse, andraspråkslärande, L2-inlärning, Natural Language Generation
National Category
Natural Language Processing
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-336531 (URN)978-91-8040-661-1 (ISBN)
Public defence
2023-10-17, F3, Lindstedtsvägen 26, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20230915

Available from: 2023-09-15 Created: 2023-09-14 Last updated: 2025-02-07Bibliographically approved

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Kalpakchi, DmytroBoye, Johan

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