Grammatical Error Correction in Low-Resource Scenarios
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405590" target="_blank" >RIV/00216208:11320/19:10405590 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/D19-5545/" target="_blank" >https://www.aclweb.org/anthology/D19-5545/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/D19-5545" target="_blank" >10.18653/v1/D19-5545</a>
Alternative languages
Result language
angličtina
Original language name
Grammatical Error Correction in Low-Resource Scenarios
Original language description
Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
ISBN
978-1-950737-84-0
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
346-356
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Hong Kong, China
Event date
Nov 4, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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