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2017 | OriginalPaper | Buchkapitel

Neural Machine Translation for Morphologically Rich Languages with Improved Sub-word Units and Synthetic Data

verfasst von : Mārcis Pinnis, Rihards Krišlauks, Daiga Deksne, Toms Miks

Erschienen in: Text, Speech, and Dialogue

Verlag: Springer International Publishing

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Abstract

This paper analyses issues of rare and unknown word splitting with byte pair encoding for neural machine translation and proposes two methods that allow improving the quality of word splitting. The first method linguistically guides byte pair encoding and the second method limits splitting of unknown words. We also evaluate corpus re-translation for a new language pair – English-Latvian. We show a significant improvement in translation quality over baseline systems in all reported experiments. We envision that the proposed methods will allow improving the translation of named entities and technical texts in production systems that often receive data not represented in the training corpus.

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Nächstes Kapitel Evaluation of Dictionary Creating Methods for Under-Resourced Languages

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Titel: Neural Machine Translation for Morphologically Rich Languages with Improved Sub-word Units and Synthetic Data
verfasst von: Mārcis Pinnis
Rihards Krišlauks
Daiga Deksne
Toms Miks
Verlag: Springer International Publishing
Buch: Text, Speech, and Dialogue
Print ISBN: 978-3-319-64205-5

Electronic ISBN: 978-3-319-64206-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-64206-2_27

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