Typology-aware neural dependency parsing : challenges and directions
Name
1203138358-MIT.pdf
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526.42 KB
Format
Adobe PDF
Checksum (MD5)
4aa90dd54535d3a38e150b8186c6111e
Author(s)
Fisch, Adam(Adam Joshua)
Advisor(s)
Regina Barzilay.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
This thesis explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of the investigation put forth in this thesis is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing. Code for the work in this thesis is available at https://github.com/ajfisch/TypologyParser.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-41).
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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