Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10426964" target="_blank" >RIV/00216208:11320/20:10426964 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/2020.aacl-main.12" target="_blank" >https://www.aclweb.org/anthology/2020.aacl-main.12</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
Original language description
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů