Conference paper Open Access

Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs

Philipp Wiesenbach; Stefan Riezler

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    <subfield code="u">Computational Linguistics, Heidelberg University, Germany</subfield>
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    <subfield code="a">&lt;p&gt;Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data&amp;nbsp;and their translations. We present a multi-task modeling approach to translating Middle Egyptian that is inspired by recent successful&amp;nbsp;approaches to multi-task learning in end-to-end speech translation. We leverage the phonographic aspect of the hieroglyphic writing&amp;nbsp;system, and show that similar to multi-task learning of speech recognition and translation, joint learning and sharing of structural&amp;nbsp;information between hieroglyph transcriptions, translations, and POS tagging can improve direct translation of hieroglyphs by several&amp;nbsp;BLEU points, using a minimal amount of manual transcriptions.&lt;/p&gt;</subfield>
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