3524924
doi
10.5281/zenodo.3524924
oai:zenodo.org:3524924
user-iwslt2019
Stefan Riezler
Computational Linguistics & IWR, Heidelberg University, Germany
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs
Philipp Wiesenbach
Computational Linguistics, Heidelberg University, Germany
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data and their translations. We present a multi-task modeling approach to translating Middle Egyptian that is inspired by recent successful approaches to multi-task learning in end-to-end speech translation. We leverage the phonographic aspect of the hieroglyphic writing system, and show that similar to multi-task learning of speech recognition and translation, joint learning and sharing of structural information between hieroglyph transcriptions, translations, and POS tagging can improve direct translation of hieroglyphs by several BLEU points, using a minimal amount of manual transcriptions.</p>
Zenodo
2019-11-02
info:eu-repo/semantics/conferencePaper
3524923
user-iwslt2019
1579538651.112103
142447
md5:00f27e13d51c7a31de7ee82ddfd5eed9
https://zenodo.org/records/3524924/files/IWSLT2019_paper_2.pdf
public
10.5281/zenodo.3524923
isVersionOf
doi