Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training

Autor:	Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, Antonio Toral
Přispěvatelé:	Artificial Intelligence
Zdroj:	University of Groningen Proceedings of the Fifth Conference on Machine Translation (WMT), 274-281 STARTPAGE=274;ENDPAGE=281;TITLE=Proceedings of the Fifth Conference on Machine Translation (WMT)
Popis:	Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9ecd58399fcf0289fbc5c12b0518484e https://research.rug.nl/en/publications/ce246963-3b30-4064-ab65-ae9e5e506c5e Zobrazit plný text záznamu