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Incorporating Knowledge into Question Answering Systems

Student: Arina Rak

Supervisor: Evgeny Sokolov

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Incorporating knowledge into Natural Language Processing (NLP) models has been gaining popularity in recent years. It provides the NLP systems with additional background information, making them more similar to people relying not only on the text in front of them but also on their own experience while solving NLP tasks. This paper aims to aid a question answering system dealing with long contexts by proposing a two-stage denoising pipeline. We significantly decrease the computation and memory resources required for training and simplify its task by reducing irrelevant information.

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