relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3511/
title: QASA: Advanced Document Retriever for Open-Domain Question Answering by Learning to Rank Question-Aware Self-Attentive Document Representations
creator: Nguyen, Minh Trang
creator: Tran, Van Lien
creator: Can, Duy Cat
creator: Ha, Quang Thuy
creator: Vu, Thi Ly
creator: Chng, Eng-Siong
subject: Information Technology (IT)
description: 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 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 is lagged behind. In this work, we propose a novel encoding method for learning question-aware self-attentive document representations. By applying pair-wise ranking approach to these encodings, we build 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.
publisher: ACM New York, NY, USA ©2019
date: 2019-01-25
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc_nd
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3511/1/C026.pdf
identifier:   Nguyen, Minh Trang and Tran, Van Lien and Can, Duy Cat and Ha, Quang Thuy and Vu, Thi Ly and Chng, Eng-Siong  (2019) QASA: Advanced Document Retriever for Open-Domain Question Answering by Learning to Rank Question-Aware Self-Attentive Document Representations.  In: The 3rd International Conference on Machine Learning and Soft Computing, 25-28 January 2019, Da Lat, Vietnam.     
relation: https://dl.acm.org/citation.cfm?id=3310999