Source-Free Transductive Transfer Learning for Structured Prediction
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AZA46IJ2H" target="_blank" >RIV/00216208:11320/25:ZA46IJ2H - isvavai.cz</a>
Výsledek na webu
<a href="https://rest.neptune-prod.its.unimelb.edu.au/server/api/core/bitstreams/01fd4b6c-e35d-409a-9fa4-c1e83aab1831/content" target="_blank" >https://rest.neptune-prod.its.unimelb.edu.au/server/api/core/bitstreams/01fd4b6c-e35d-409a-9fa4-c1e83aab1831/content</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Source-Free Transductive Transfer Learning for Structured Prediction
Popis výsledku v původním jazyce
Current transfer learning approaches require two strong assumptions: the source domain data is available and the target domain has labelled data. These assumptions are problematic when both the source domain data is private and the target domain has no labelled data. Thus, we consider the source-free unsupervised transfer setup in which the assumptions are violated across both languages and domains (genres). To transfer structured prediction models in the source-free setting, we propose two methods: Parsimonious Parser Transfer (PPT) designed for single-source transfer of dependency parsers across languages, and PPTX which is the multi-source version of PPT.
Název v anglickém jazyce
Source-Free Transductive Transfer Learning for Structured Prediction
Popis výsledku anglicky
Current transfer learning approaches require two strong assumptions: the source domain data is available and the target domain has labelled data. These assumptions are problematic when both the source domain data is private and the target domain has no labelled data. Thus, we consider the source-free unsupervised transfer setup in which the assumptions are violated across both languages and domains (genres). To transfer structured prediction models in the source-free setting, we propose two methods: Parsimonious Parser Transfer (PPT) designed for single-source transfer of dependency parsers across languages, and PPTX which is the multi-source version of PPT.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
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Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů