Graduate program in Computer Engineering.Özgür, Arzucan.Arısoy Saraçlar, Ebru.Manav, Yusufcan.2025-04-142025-04-142023Graduate program in Computer Engineering. TKL 2023 U68 PhD (Thes TKL 2023 Z37https://digitalarchive.library.bogazici.edu.tr/handle/123456789/21500This thesis focuses on employing a question-generation system to improve the performance of question-answering models. We propose a multitask-trained questiongeneration module that is built on a multilingual encoder-decoder architecture and can produce question-answer pairs over plain text passages. We were able to adapt the question-generation system to several languages by using a multilingual model. First, we created a Turkish Question Answering dataset utilizing the Turkish Wikipedia pages and this question-generation system. Our experiments revealed that the performance on the Turkish XQuAD set was enhanced by 3% when the generated dataset was combined with the human-annotated dataset for question-answering model training. Second we also extensively test our model in many languages and low-resource environments. We used limited annotated data from the question-answering datasets from different languages like English, German, French, and Turkish; to train the question generation model. We then utilized this model to create artificial question-answer pairs from the unannotated paragraphs. Our experiments revealed that, especially in the lower data settings, our augmentation strategy consistently outperformed the baseline question- answering models that are trained on human-annotated data across a range of dataset sizes and languages.Question-answering systems.Natural language processing (Computer science)xiii, 76 leaves