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        <title>Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B</title>
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        <namePart type="given">Jiaming</namePart>
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    <name type="personal">
        <namePart type="given">Regina</namePart>
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            <namePart type="given">David</namePart>
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    <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 unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to decipherment of Ugaritic, we achieve 5% absolute improvement over state-of-the-art results. We also report first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates.</abstract>
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%0 Conference Proceedings
%T Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
%A Luo, Jiaming
%A Cao, Yuan
%A Barzilay, Regina
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F luo-etal-2019-neural
%X 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 unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to decipherment of Ugaritic, we achieve 5% absolute improvement over state-of-the-art results. We also report first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates.
%R 10.18653/v1/P19-1303
%U https://aclanthology.org/P19-1303
%U https://doi.org/10.18653/v1/P19-1303
%P 3146-3155

Markdown (Informal)

[Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B](https://aclanthology.org/P19-1303) (Luo et al., ACL 2019)

• Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B (Luo et al., ACL 2019)

ACL

• Jiaming Luo, Yuan Cao, and Regina Barzilay. 2019. Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3146–3155, Florence, Italy. Association for Computational Linguistics.