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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.cawl-1.7.pdf