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NeuroDecipher

This repo hosts the codebase for reproduce the results for the ACL paper Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B. Still cleaning up some part of the code. Stay tuned for updates.

Data

Data for linear B and Ugaritic are included in the data folder:

  • uga-heb.no_spe.cog is the entire Ugaritic-Hebrew data obtained from Ben Snyder. no_spe stands for no special symbols since the original file contains special symbols that mark the morphological segmentations and affixes.
  • uga-heb.small.no_spe.cog is the exact random subset of Ugaritic data I used in the paper for training the model. Around one tenth of the original file.
  • linear_b-greek.cog is the linear B data used in the paper. notebooks/Linear_b_simplified.ipynb is the same notebook I used for preparing the linear B data.
  • linear_b-greek.names.cog is the linear B data that only included names on the Greek side.

Note that you might need to install fonts in order to render Linear B scripts properly in your computer.

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  • Python 71.4%
  • Jupyter Notebook 23.0%
  • C++ 5.2%
  • C 0.4%