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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Harrison, Andrea M."],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Master of Arts","degree_level":"Master","degree_discipline":"Linguistics","degree_department":null,"school":null,"contributors":[],"advisors":["Kyle Gorman"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-02-01T08:00:00Z","date_published":"2024-02-01T08:00:00Z","updated_at":"2026-07-24T01:58:49Z","subjects":["Computational Linguistics","Low-resource languages","Finno-Ugric languages","Uralic languages","Ingrian","Computational morphology","Morphophonology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/5677","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kyle Gorman"]},{"key":"dc:creator","label":"Author","values":["Harrison, Andrea M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-01-30T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Linguistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Arts"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computational Linguistics","Low-resource languages","Finno-Ugric languages","Uralic languages","Ingrian","Computational morphology","Morphophonology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/5677"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Consonant (De)gradation in Ingrian?"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kyle Gorman"],"dc:creator":["Harrison, Andrea M."],"dc:date.available":["2024-01-30T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/5677"],"dc:subject":["Computational Linguistics","Low-resource languages","Finno-Ugric languages","Uralic languages","Ingrian","Computational morphology","Morphophonology"],"dc:title":["Consonant (De)gradation in Ingrian?"],"thesis:degree_discipline":["Linguistics"],"thesis:degree_level":["Master"],"thesis:degree_name":["Master of Arts"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:58:49Z"}