Work

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

Year: 2019

Type: conference-paper

Abstract: 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.1

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p19-1303

Authors: Jiaming Luo, Yuan Cao, Regina Barzilay

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Google (United States)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Office of the Director of National Intelligence, Intelligence Advanced Research Projects Activity, Quest for Intelligence, Massachusetts Institute of Technology, NIH Office of the Director

Award: G8810978190