Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?

Anthology ID:: 2021.findings-acl.75
Original:: 2021.findings-acl.75v1
Version 2:: 2021.findings-acl.75v2
Volume:: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:: August
Year:: 2021
Address:: Online
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 847–861
Language:
URL:: https://aclanthology.org/2021.findings-acl.75
DOI:: 10.18653/v1/2021.findings-acl.75
Bibkey:
Cite (ACL):: Clémentine Fourrier, Rachel Bawden, and Benoît Sagot. 2021. Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 847–861, Online. Association for Computational Linguistics.
Cite (Informal):: Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task? (Fourrier et al., Findings 2021)
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PDF:: https://preview.aclanthology.org/paclic-22-ingestion/2021.findings-acl.75.pdf
Video:: https://preview.aclanthology.org/paclic-22-ingestion/2021.findings-acl.75.mp4
Code: clefourrier/coppermt