Scarcity of training data continues to pose a problem for the development of neural machine translation systems for low-resource languages. This study develops a method for the incorporation of linguistic information into the training of neural machine translation models for low-resource languages, using morphological grammars created using finite state transducers. This study explores the benefits, historical background, and effectiveness of this approach. This study incorporates morphological tags into a pre-trained multilingual neural machine translation model using a dual encoder structure. This study finds an improvement in performance in the Irish-English translation scenario. This method offers promising results with low computational requirements, and contributes to the understanding of techniques for low resource neural machine translation.
","abstract_html":"<p>Scarcity of training data continues to pose a problem for the development of neural machine translation systems for low-resource languages. This study develops a method for the incorporation of linguistic information into the training of neural machine translation models for low-resource languages, using morphological grammars created using finite state transducers. This study explores the benefits, historical background, and effectiveness of this approach. This study incorporates morphological tags into a pre-trained multilingual neural machine translation model using a dual encoder structure. This study finds an improvement in performance in the Irish-English translation scenario. This method offers promising results with low computational requirements, and contributes to the understanding of techniques for low resource neural machine translation.</p>","abstract_has_math":false,"creators":["Uva, Nicholas J"],"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":2025,"date_issued":"2025-02-01T08:00:00Z","date_published":"2025-02-01T08:00:00Z","updated_at":"2026-07-24T01:59:21Z","subjects":["Computational Linguistics","linguistics","natural language processing","translation","machine translation","low-resource","finite state","finite state grammar","morphology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/6124","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":["Uva, Nicholas J"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-16T08:00:00Z"]},{"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","linguistics","natural language processing","translation","machine translation","low-resource","finite state","finite state grammar","morphology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/6124"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Scarcity of training data continues to pose a problem for the development of neural machine translation systems for low-resource languages. This study develops a method for the incorporation of linguistic information into the training of neural machine translation models for low-resource languages, using morphological grammars created using finite state transducers. This study explores the benefits, historical background, and effectiveness of this approach. This study incorporates morphological tags into a pre-trained multilingual neural machine translation model using a dual encoder structure. This study finds an improvement in performance in the Irish-English translation scenario. This method offers promising results with low computational requirements, and contributes to the understanding of techniques for low resource neural machine translation.
"]},{"key":"dc:title","label":"Title","values":["Improving Low-Resource Translation with Finite State Grammars"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kyle Gorman"],"dc:creator":["Uva, Nicholas J"],"dc:date.available":["2025-01-16T08:00:00Z"],"dc:description.abstract":["Scarcity of training data continues to pose a problem for the development of neural machine translation systems for low-resource languages. This study develops a method for the incorporation of linguistic information into the training of neural machine translation models for low-resource languages, using morphological grammars created using finite state transducers. This study explores the benefits, historical background, and effectiveness of this approach. This study incorporates morphological tags into a pre-trained multilingual neural machine translation model using a dual encoder structure. This study finds an improvement in performance in the Irish-English translation scenario. This method offers promising results with low computational requirements, and contributes to the understanding of techniques for low resource neural machine translation.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/6124"],"dc:subject":["Computational Linguistics","linguistics","natural language processing","translation","machine translation","low-resource","finite state","finite state grammar","morphology"],"dc:title":["Improving Low-Resource Translation with Finite State Grammars"],"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":"2026-07-24T01:59:21Z"}