QASA : advanced document retriever for open-domain question answering by learning to rank question-aware self-attentive document representations

• Main Authors: Nguyen, Trang M., Tran, Van-Lien, Can, Duy-Cat, Ha, Quang-Thuy, Vu, Ly T., Chng, Eng-Siong
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Open-domain Question Answering; Document Retrieval
• Online Access: https://hdl.handle.net/10356/144883
• Institution: Nanyang Technological University

For information consumers, being able to obtain a short and accurate answer for a 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. While the problem of machine comprehension has rece...