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

ACL 2019 Jiaming LuoYuan CaoRegina Barzilay

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... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.