@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.",
}

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.
Born, Logan Monroe, M. Willis Kelley, Kathryn Sarkar, Anoop
Towards Fast Cognate Alignment on Imbalanced Data