The resulting model is a Document Retriever, called QASA, which is then integrated with a machine reader to form a complete open-domain QA system. Our system is thoroughly evaluated using QUASAR-T dataset and shows surpassing results compared to other state-of-the-art methods. | Luận án Tiến sĩ Khoa học máy tính Advanced deep learning methods and applications in open domain question answering VIETNAM NATIONAL UNIVERSITY HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY Nguyen Minh Trang ADVANCED DEEP LEARNING METHODS AND APPLICATIONS IN OPEN-DOMAIN QUESTION ANSWERING MASTER THESIS Major Computer Science HA NOI - 2019 VIETNAM NATIONAL UNIVERSITY HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY Nguyen Minh Trang ADVANCED DEEP LEARNING METHODS AND APPLICATIONS IN OPEN-DOMAIN QUESTION ANSWERING MASTER THESIS Major Computer Science Supervisor . Ha Quang Thuy . Nguyen Ba Dat HA NOI - 2019 Abstract Ever since the Internet has become ubiquitous the amount of data accessible by information retrieval systems has increased exponentially. As for information con- sumers being able to obtain a short and accurate answer for any query is one of the most desirable features. This motivation along with the rise of deep learning has led to a boom in open-domain Question Answering QA research. An open- domain QA system usually consists of two modules retriever and reader. Each is developed to solve a particular task. While the problem of document compre- hension has received multiple success with the help of large training corpora and the emergence of attention mechanism the development of document retrieval in open-domain QA has not gain much progress. In this thesis we propose a novel encoding method for learning question-aware self-attentive document represen- tations. Then these representations are utilized by applying pair-wise ranking approach to them. The resulting model is a Document Retriever called QASA which is then integrated with a machine reader to form a complete open-domain QA system. Our system is thoroughly evaluated using QUASAR-T dataset and shows surpassing results compared to other state-of-the-art methods. Keywords Open-domain Question Answering Document Retrieval Learning to Rank Self-attention mechanism. iii Acknowledgements
