Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
© 2019 Association for Computational Linguistics 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. T...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computational Linguistics (ACL)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137421.2 |