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dc.contributor.authorHill, Nathanen
dc.date.accessioned2022-07-22T08:39:57Z
dc.date.available2022-07-22T08:39:57Z
dc.date.issued2022en
dc.date.submitted2022en
dc.identifier.citationList, Johann-Mattis; Vylomova, Ekaterina; Forkel, Robert; Hill, Nathan W.; Cotterell, Ryan D., The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes, Proceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022), 2022, 52 - 62en
dc.identifier.otherYen
dc.descriptionPUBLISHEDen
dc.description.abstractThis 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
dc.format.extent52en
dc.format.extent62en
dc.language.isoenen
dc.relation.ispartofseriesProceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022)en
dc.rightsYen
dc.titleThe SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexesen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/hillnaen
dc.identifier.rssinternalid244738en
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeDigital Humanitiesen
dc.identifier.orcid_id0000-0001-6423-017Xen
dc.identifier.urihttp://hdl.handle.net/2262/100312


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