Automated question generation and question answering from Turkish texts


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Akyön F. Ç., Çavuşoğlu D., Cengiz C., Altinuç S. O., TEMİZEL A.

Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.5, pp.1931-1940, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.55730/1300-0632.3914
  • Journal Name: Turkish Journal of Electrical Engineering and Computer Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1931-1940
  • Keywords: answer extraction, multitask, question answering, question generation, transformer, Turkish
  • Middle East Technical University Affiliated: Yes

Abstract

© 2022 Turkiye Klinikleri. All rights reserved.While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the best of our knowledge, this is the first academic work that performs automated text-to-text question generation from Turkish texts. Experimental evaluations show that the proposed multitask setting achieves state-of-the-art Turkish question answering and question generation performance on TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and the pretrained models are available at https://github.com/obss/turkish-question-generation.