Learning Deep Representations for Low-Resource Cross-Lingual Natural Language Processing
Loading...
No Access Until
Permanent Link(s)
Collections
Other Titles
Authors
Abstract
Large-scale annotated datasets are an indispensable ingredient of modern Natural Language Processing (NLP) systems. Unfortunately, most labeled data is only available in a handful of languages; for the vast majority of human languages, few or no annotations exist to empower automated NLP technology. Cross-lingual transfer learning enables the training of NLP models using labeled data from other languages, which has become a viable technique for building NLP systems for a wider spectrum of world languages without the prohibitive need for data annotation. Existing methods for cross-lingual transfer learning, however, require cross-lingual resources (e.g. machine translation systems) to transfer models across languages. These methods are hence futile for many low-resource languages without such resources. This dissertation proposes a deep representation learning approach for low-resource cross-lingual transfer learning, and presents several models that (i) progressively remove the need for cross-lingual supervision, and (ii) go beyond the standard bilingual transfer case into the more realistic multilingual setting. By addressing key challenges in two important sub-problems, namely multilingual lexical representation and model transfer, the proposed models in this dissertation are able to transfer NLP models across multiple languages with no cross-lingual resources.
Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
2019-05-30
Publisher
Keywords
natural language processing; Deep Learning; Artificial intelligence; Computer science; Adversarial Neural Networks; Cross-Lingual; Multilingual; Transfer Learning
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Cardie, Claire T.
Committee Co-Chair
Committee Member
Kleinberg, Jon M.
Hopcroft, John E.
Hopcroft, John E.
Degree Discipline
Computer Science
Degree Name
Ph.D., Computer Science
Degree Level
Doctor of Philosophy
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Attribution 4.0 International
Rights URI
Types
dissertation or thesis