Fine-Grained Language Relatedness for Zero-Shot Silesian-English Translation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00136158" target="_blank" >RIV/00216224:14330/23:00136158 - isvavai.cz</a>
Result on the web
<a href="https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=153" target="_blank" >https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=153</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Fine-Grained Language Relatedness for Zero-Shot Silesian-English Translation
Original language description
When parallel corpora are not available to train or fine-tune Machine Translation (MT) systems, one solution is to use data from a related language, and operate in a zero-shot setting. We explore the behaviour and performance of two pre-trained Large Language Models (LLMs) for zero-shot Silesian-English translation, by fine-tuning them on increasingly related languages. Our experiment shows that using data from related languages generally improves the zero-shot translation performance for our language pair, but the optimal fine-grained choice inside the Slavic language family is non-trivial and depends on the model characteristics.
Czech name
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Czech description
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Project
<a href="/en/project/LM2018101" target="_blank" >LM2018101: Digital Research Infrastructure for the Language Technologies, Arts and Humanities</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
RASLAN 2023 Recent Advances in Slavonic Natural Language Processing
ISBN
9788026317937
ISSN
2336-4289
e-ISSN
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Number of pages
12
Pages from-to
145-156
Publisher name
Tribun EU
Place of publication
Brno
Event location
Kouty nad Desnou
Event date
Jan 1, 2023
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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