@inproceedings{born-etal-2024-towards,
title = "Towards Fast Cognate Alignment on Imbalanced Data",
author = "Born, Logan and
Monroe, M. Willis and
Kelley, Kathryn and
Sarkar, Anoop",
editor = "Gorman, Kyle and
Prud'hommeaux, Emily and
Roark, Brian and
Sproat, Richard",
booktitle = "Proceedings of the Second Workshop on Computation and Written Language (CAWL) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.cawl-1.7",
pages = "53--58",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Fast Cognate Alignment on Imbalanced Data
%A Born, Logan
%A Monroe, M. Willis
%A Kelley, Kathryn
%A Sarkar, Anoop
%Y Gorman, Kyle
%Y Prud’hommeaux, Emily
%Y Roark, Brian
%Y Sproat, Richard
%S Proceedings of the Second Workshop on Computation and Written Language (CAWL) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F born-etal-2024-towards
%X 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.
%U https://aclanthology.org/2024.cawl-1.7
%P 53-58
Markdown (Informal)
[Towards Fast Cognate Alignment on Imbalanced Data](https://aclanthology.org/2024.cawl-1.7) (Born et al., CAWL-WS 2024)
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.