Turkish Journal of Electrical Engineering and Computer Sciences
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.
Turkish, question answering, question generation, answer extraction, multitask, transformer
AKYÖN, FATİH ÇAĞATAY; ÇAVUŞOĞLU, ALİ DEVRİM EKİN; CENGİZ, CEMİL; ALTINUÇ, SİNAN ONUR; and TEMİZEL, ALPTEKİN
"Automated question generation and question answering from Turkish texts,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
5, Article 17.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss5/17
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