An Overview of Existing Methods for Automatic Generation of Test Tasks in Natural Language
( Pp. 46-55)

Recently, in the field of education, much attention has been paid to the use of multiple choice questions as a tool for assessing knowledge. The development of test tasks requires a lot of time and is highly labor intensive. It is difficult to perform such a task manually, so many researchers offer various ways and approaches to automate the creation of test tasks in natural language. In this paper, we present an overview of scientific achievements in the field of automatic question generation, which examines the classification of question generation systems by dividing them into five groups: machine learning-based methods, neural network-based, tree-based, rule-based or template-based and hybrid methods.

Maslova M.A. An Overview of Existing Methods for Automatic Generation of Test Tasks in Natural Language. Computational Nanotechnology. 2023. Vol. 10. No. 4. Pp. 46–55. (In Rus.) DOI: 10.33693/2313-223X-2023-10-4-46-55. EDN: CBFUQS

Agarwal R., Negi V., Kalra A., Mittal A. Deep learning and linguistic feature based automatic multiple choice question generation from text. In: International Conference on Distributed Computing and Intelligent Technology. 2022. Vol. 13145. Pp. 260–264. URL: https://doi.org/10.1007/978-3-030-94876-4_18
Aithal S.G., Rao A.B., Singh S. Automatic question-answer pairs generation and question similarity mechanism in question answering system. Applied Intelligence. 2021. No. 51. Pp. 8484–8497. URL: https://doi.org/10.1007/s10489-021-02348-9
Azevedo P., Leite B., Cardoso H.L. et al. Exploring NLP and information extraction to jointly address question generation and answering. In: International Conference on Artificial Intelligence Applications and Innovations. 2020. Vol. 584. Pp. 396–407. URL: https://doi.org/10.1007/978-3-030-49186-4_33
Chen Y., Wu L., Zaki M.J. Reinforcement learning based graph-to-sequence model for natural question generation. In: International Conference on Learning Representations. 2019. URL: https://arxiv.org/abs/1908.04942
Chomphooyod P., Suchato A., Tuaycharoen N., Punyabukkana Pr. English grammar multiple-choice question generation using Text-to-Text Transfer Transformer. Computers and Education: Artificial Intelligence. 2023. Vol. 5. URL: https://doi.org/10.1016/j.caeai.2023.100158.
Conejo R., del Campo-Ávila J., Barros B. First steps towards automatic question generation and assessment of LL(1) grammars. In: International Conference on Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. 2022. Vol. 13356. Pp. 271–275. URL: https://doi.org/10.1007/978-3-031-11647-6_50
Das B., Majumder M., Phadikar S., Ahmed Sk.A. Automatic generation of fill-in-the-blank question with corpus-based distractors for E-assessment to enhance learning. Computer Applications in Engineering Education. 2019. No. 27. Pp. 1485–1495.
Das B., Majumder M., Phadikar S., Sekh A.A. Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment. Multimedia Tools and Applications. 2021. No. 80. Pp. 31907–31925. URL: https://doi.org/10.1007/s11042-021-11222-2
Fung Y., Kwok J.C., Lee L. et al. Automatic question generation system for English reading comprehension. ICTE: Technology in Education. Innovations for Online Teaching and Learning. 2020. Pp. 136–146.
Guan M., Mondal S.K., Dai H.N., Bao H. Reinforcement learning-driven deep question generation with rich semantics. Information Processing and Management. 2023. Vol. 60. No. 2. URL: https://doi.org/10.1016/j.ipm.2022.103232
Kim Ya., Lee Hw., Shin J., Jung K. Improving neural question generation using answer separation. In: The Thirty-Third AAAI Conference on Artificial Intelligence. 2019. Pp. 6602–6609.
Kumar A.P., Nayak A., K M.S. et al. A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques. International Journal of Artificial Intelligence in Education. 2023. URL: https://doi.org/10.1007/s40593-023-00333-6
Kusuma S.F., Siahaan D.O., Fatichah Ch. Automatic question generation with various difficulty levels based on knowledge ontology using a query template. Knowledge-Based Systems. 2022. Vol. 249. URL: https://doi.org/10.1016/j.knosys.2022.108906
Leo J., Kurdi G., Matentzoglu N. et al. Ontology-based generation of medical, multi-term MCQ. International Journal of Artificial Intelligence in Education. 2019. No. 29. Pp. 145–188.
Li W. A semantics-based approach to automatic generation of test questions and automatic verification of user answers in the intelligent tutoring systems. Open Semantic Technologies for Intelligent Systems Design. 2022. No. 6. Pp. 381–394.
Li W., Grakova N., Qian L. Ontological approach to automating the processes of question generation and knowledge control in intelligent learning systems. Open Semantic Technologies for Designing Intelligent Systems. 2020. No. 4. Pp. 215–224.
Murugan Sh., Ramakrishnan B.S. Automatic morpheme-based distractors generation for fill-in-the-blank questions using listwise learning-to-rank method for agglutinative language. Engineering Science and Technology. 2022. Vol. 26. URL: https://doi.org/10.1016/j.jestch.2021.04.012
Riza L.S., Firdaus Y., Sukamto R.A., Samah W.Kh.A.F.A. Automatic generation of short-answer questions in reading comprehension using NLP and KNN. Multimedia Tools and Applications. 2023. No. 82. Pp. 41913–41940. URL: https://doi.org/10.1007/s11042-023-15191-6
Rodriguez-Torrealba R., Garcia-Lopez E., Garcia-Cabot A. End-to-end generation of multiple-choice questions using text-to-text transfer transformer models. Expert Systems with Applications. 2022. Vol. 208. URL: https://doi.org/10.1016/j.eswa.2022.118258
Shuai P., Li L., Liu S., Shen J. QDG: A unified model for automatic question-distractor pairs generation. Applied Intelligence. 2023. No. 53. Pp. 8275–8285. URL: https://doi.org/10.1007/s10489-022-03894-6
Steuer T., Filighera A., Rensing Ch. Remember the Facts? Investigating Answer-Aware Neural Question Generation for Text Comprehension. In: International Conference on Artificial Intelligence in Education. 2020. Vol. 12163. URL: https://doi.org/10.1007/978-3-030-52237-7_A
Wang H.C., Maslim M., Kan C.H. A question–answer generation system for an asynchronous distance learning platform. Education and Information Technologies. 2023. No. 28. Pp. 12059–12088. URL: https://doi.org/10.1007/s10639-023-11675-y
Wang Y., Rong W., Zhang J. et al. Multi-turn dialogue-oriented pretrained question generation model. Complex & Intelligent Systems. 2020. No. 6. Pp. 493–505. URL: https://doi.org/10.1007/s40747-020-00147-2
Astrashab V.V., Kalugina M.A., Klebanov D.A. et al. Neural network for generating questions to the text. In: International scientific practice. conf. “BIG DATA and Advanced Analytics. BIG DATA and high-level analysis”. 2020. No. 6. Pp. 363–369. (In Rus.)
Desyatirikov F.A. A systematic approach to the organization of automatic testing of students of musical educational institutions. In: XXV International Scientific and Educational-Practical Conference. 2021. Vol. 3. Pp. 452–458.
Lee V. An ontological approach to automatic question generation in intelligent learning systems. Reports of BGUIR. 2020. Vol. 18. No. 5. Pp. 44–52. (In Rus.)

automatic generation of test tasks, automatic generation of test questions, natural language processing, natural language generation.