Transferring Neural Potentials For High Order Dependency Parsing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AGK8FGIEG" target="_blank" >RIV/00216208:11320/23:GK8FGIEG - isvavai.cz</a>
Result on the web
<a href="http://arxiv.org/abs/2306.10469" target="_blank" >http://arxiv.org/abs/2306.10469</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Transferring Neural Potentials For High Order Dependency Parsing
Original language description
"High order dependency parsing leverages high order features such as siblings or grandchildren to improve state of the art accuracy of current first order dependency parsers. The present paper uses biaffine scores to provide an estimate of the arc scores and is then propagated into a graphical model. The inference inside the graphical model is solved using dual decomposition. The present algorithm propagates biaffine neural scores to the graphical model and by leveraging dual decomposition inference, the overall circuit is trained end-to-end to transfer first order informations to the high order informations."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů