The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
dc.contributor.author
List, Johann-Mattis
dc.contributor.author
Vylomova, Ekaterina
dc.contributor.author
Forkel, Robert
dc.contributor.author
Hill, Nathan
dc.contributor.author
Cotterell, Ryan
dc.contributor.editor
Vylomova, Ekaterina
dc.contributor.editor
Ponti, Edoardo
dc.contributor.editor
Cotterell, Ryan
dc.date.accessioned
2023-04-13T12:46:21Z
dc.date.available
2022-12-21T12:00:59Z
dc.date.available
2023-04-13T12:46:21Z
dc.date.issued
2022-07
dc.identifier.isbn
978-1-955917-93-3
en_US
dc.identifier.other
10.18653/v1/2022.sigtyp-1.7
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/588603
dc.identifier.doi
10.3929/ethz-b-000588603
dc.description.abstract
This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computational Linguistics
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.book.title
Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
en_US
ethz.pages.start
52
en_US
ethz.pages.end
62
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP (SIGTYP 2022)
en_US
ethz.event.location
Seattle, WA, USA
en_US
ethz.event.date
July 14, 2022
en_US
ethz.publication.place
Stroudsburg, PA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09682 - Cotterell, Ryan / Cotterell, Ryan
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09682 - Cotterell, Ryan / Cotterell, Ryan
en_US
ethz.relation.isSupplementedBy
https://github.com/sigtyp/ST2022
ethz.relation.isSupplementedBy
10.5281/zenodo.6586772
ethz.relation.isPartOf
handle/20.500.11850/607705
ethz.date.deposited
2022-12-21T12:00:59Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-04-13T12:46:27Z
ethz.rosetta.lastUpdated
2024-02-02T21:36:48Z
ethz.rosetta.versionExported
true
ethz.COinS
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