Automatic question generation for improving low resource question answering performance

Abstract

This 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.