Towards knowledge-based, robust question answering
Name
1192966860-MIT.pdf
Size
2.11 MB
Format
Adobe PDF
Checksum (MD5)
1f36e3c7ee8461c0dc6db022d7835343
Author(s)
Min, So Yeon,S.M.Massachusetts Institute of Technology.
Advisor(s)
Peter Szolovits.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Current question answering systems face two major challenges; the ability to employ external knowledge and to robustly generalize to unseen expressions of questions need to be improved. In this thesis, I introduce two works that can together help advance question answering. First, I introduce TransINT, a novel and interpretable knowledge graph embedding method that isomorphically preserves the implication ordering among relations in the embedding space. Second, I present methods to train sequence-to-sequence semantic parsing models robust to unseen paraphrases. These two works could together serve as steps to create human-like question answering systems that can understand unseen paraphrases and link existing and external facts for logical inference.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 63-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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