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Erschienen in:
Data Science and Advanced Digital Technologies Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Effective Online Learning Implementation for Statistical Machine Translation

verfasst von : Toms Miks, Mārcis Pinnis, Matīss Rikters, Rihards Krišlauks

Erschienen in: Databases and Information Systems

Verlag: Springer International Publishing

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Abstract

Online learning has been an active research area in statistical machine translation. However, as we have identified in our research, the implementation of successful online learning capabilities in the Moses SMT system can be challenging. In this work, we show how to use open source and freely available tools and methods in order to successfully implement online learning for SMT systems that allow improving translation quality. In our experiments, we compare the baseline implementation in Moses to an improved implementation utilising a two-step tuning strategy. We show that the baseline implementation achieves unstable performance (from −6 to \(+\)6 BLEU points in online learning scenarios and over −6 BLEU points in translation scenarios, i.e., when post-edits were not returned to the SMT system). However, our devised two-step tuning strategy is able to successfully utilise online learning capabilities and is able to improve MT quality in the online learning scenario by up to \(+\)12 BLEU points.

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Vorheriges Kapitel Semi-automatic Quasi-morphological Word Segmentation for Neural Machine Translation
Nächstes Kapitel Investigation of Text Attribution Methods Based on Frequency Author Profile
Fußnoten
1
More information about the QT21 project can be found online at http://​www.​qt21.​eu/​.
 
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Metadaten
Titel
Effective Online Learning Implementation for Statistical Machine Translation
verfasst von
Toms Miks
Mārcis Pinnis
Matīss Rikters
Rihards Krišlauks
Copyright-Jahr
2018
Buch
Databases and Information Systems

Print ISBN: 978-3-319-97570-2

Electronic ISBN: 978-3-319-97571-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-97571-9_24

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