Towards Fast Cognate Alignment on Imbalanced Data

Logan Born, M. Willis Monroe, Kathryn Kelley, Anoop Sarkar


Abstract
Cognate alignment models purport to enable decipherment, but their speed and need for clean data can make them unsuitable for realistic decipherment problems. We seek to draw attention to these shortcomings in the hopes that future work may avoid them, and we outline two techniques which begin to overcome the described problems.
Anthology ID:
2024.cawl-1.7
Volume:
Proceedings of the Second Workshop on Computation and Written Language (CAWL) @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Kyle Gorman, Emily Prud'hommeaux, Brian Roark, Richard Sproat
Venues:
CAWL | WS
SIG:
SIGWrit
Publisher:
ELRA and ICCL
Note:
Pages:
53–58
Language:
URL:
https://aclanthology.org/2024.cawl-1.7
DOI:
Bibkey:
Cite (ACL):
Logan Born, M. Willis Monroe, Kathryn Kelley, and Anoop Sarkar. 2024. Towards Fast Cognate Alignment on Imbalanced Data. In Proceedings of the Second Workshop on Computation and Written Language (CAWL) @ LREC-COLING 2024, pages 53–58, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Towards Fast Cognate Alignment on Imbalanced Data (Born et al., CAWL-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.cawl-1.7.pdf