# Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B

arXiv: https://arxiv.org/abs/1906.06718 · HTML: https://mlsanity.com/paper/1906.06718/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2019.

## Built on by (most cited first, 7 of 7)

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- [Decipherment-Aware Multilingual Learning in Jointly Trained Language Models](https://mlsanity.com/paper/2406.07231/) (2024)
- [We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference](https://mlsanity.com/paper/2607.12169/) (2026)
- [On the Non-Specificity of Statistical Measures Used in Script Decipherment](https://mlsanity.com/paper/2608.02999/) (2026)

## Abstract (arXiv)

In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics. The model utilizes an expressive sequence-to-sequence model to capture character-level correspondences between cognates. To effectively train the model in an unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to the decipherment of Ugaritic, we achieve a 5.5% absolute improvement over state-of-the-art results. We also report the first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates.