Dissertations, Theses, and Capstone Projects

Date of Degree

2-2024

Document Type

Thesis

Degree Name

M.A.

Program

Linguistics

Advisor

Kyle Gorman

Subject Categories

Computational Linguistics

Keywords

Low-resource languages, Finno-Ugric languages, Uralic languages, Ingrian, Computational morphology, Morphophonology

Abstract

This paper will present a dual method toward data enrichment for low-resource languages. Using Yoyodyne -- a Fairseq-inspired neural library for small-vocabulary sequence-to-sequence generation -- a morphological generation task was tested across labeled data encompassing multiple stages of enrichment for the low-resource language Ingrian. Due to limitations in the available data for Ingrian, weighted finite-state transducers (WFSTs) were used to generate an expanded vocabulary via HFST's toolkit for Uralic languages, and GiellaLT, a source for FST-driven lexica for low-resource languages. Further stages of experimentation used labeled data from related, higher-resource languages (Finnish, Estonian) to encourage cross-lingual transfer in the interest of paradigm completion.

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