Towards knowledge-based, robust question answering
Author(s)Min, So Yeon,S.M.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 63-68).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.