자료유형  

 학위논문

Control Number  

0017162352

International Standard Book Number  

9798383223857

Dewey Decimal Classification Number  

401

Main Entry-Personal Name  

Downey, C. M.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Washington., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

132 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

General Note  

Advisor: Levow, Gina-Anne;Steinert-Threlkeld, Shane.

Dissertation Note  

Thesis (Ph.D.)--University of Washington, 2024.

Summary, Etc.  

요약Advances in Natural Language Processing (NLP) over the past decade have largely been driven by the scale of data and computation used to train large neural network-based models. However, these techniques are inapplicable to the vast majority of the world's languages, which lack the vast digitized text datasets available for English and a few other very high-resource languages. In this dissertation, we present three case studies for extending NLP applications to under-resourced languages. These case studies include conducting unsupervised morphological segmentation for extremely low-resource languages via multilingual training and transfer, optimizing the vocabulary of a pre-trained cross-lingual model for specific target language(s), and specializing a pre-trained model for a low-resource language family (Uralic). Based on these case studies, we argue for three broad, guiding principles in extending NLP applications to under-resourced languages. First: where possible, robustly pre-trained models and representations should be leveraged. Second: components of pre-trained models that are not optimized for new languages should be substituted or substantially adapted. Third: targeted multilingual training provides a middle ground between the lack of adequate data to train models for individual under-resourced languages on one hand, and the diminishing returns of "massively multilingual" training on the other.

Subject Added Entry-Topical Term  

Linguistics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Language.

Index Term-Uncontrolled  

Natural Language Processing

Index Term-Uncontrolled  

Uralic

Index Term-Uncontrolled  

Multilinguality

Index Term-Uncontrolled  

Vocabulary

Added Entry-Corporate Name  

University of Washington Linguistics

Host Item Entry  

Dissertations Abstracts International. 86-01B.

Electronic Location and Access  

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Control Number  

joongbu:655700