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Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
Jiaming Luo, Yuan Cao, Regina Barzilay, 2019. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
A neural model for deciphering lost languages, guided by patterns of historical sound change and trained by casting cognate matching as a minimum-cost flow problem; tested on Ugaritic and Linear B.
What it's used for here
The cryptanalysis survey proposes its one-to-one min-cost-flow constraint for aligning frequent Voynich tokens with a Latin lexicon; four other surveys also cite it.
Cited by on this site 5 records
5 methods surveys
SRCSignal processing, cryptanalysis, mathematics, frameworks of inquiry, inductive methods
Cite it
Luo, J., Cao, Y., & Barzilay, R. (2019). Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1303
@article{luo2019neural,
author = {Luo, Jiaming and Cao, Yuan and Barzilay, Regina},
title = {Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B},
journal = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year = {2019},
doi = {10.18653/v1/p19-1303},
url = {https://doi.org/10.18653/v1/p19-1303}
}TY - JOUR TI - Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B AU - Luo, Jiaming AU - Cao, Yuan AU - Barzilay, Regina PY - 2019 JO - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics DO - 10.18653/v1/p19-1303 UR - https://doi.org/10.18653/v1/p19-1303 ER -
Luo, Jiaming et al.. “Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B.” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1303.