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2019 | OriginalPaper | Buchkapitel

Word Position Aware Translation Memory for Neural Machine Translation

verfasst von : Qiuxiang He, Guoping Huang, Lemao Liu, Li Li

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

The approach based on translation pieces is appealing for neural machine translation with a translation memory (TM), owing to its efficiency in both computation and memory consumption. Unfortunately, it is incapable of capturing sufficient contextual translation leading to a limited translation performance. This paper thereby proposes a simple yet effective approach to address this issue. Its key idea is to employ the word position information from a TM as additional rewards to guide the decoding of neural machine translation (NMT). Experiments on seven tasks show that the proposed approach yields consistent gains particularly for those source sentences whose TM is very similar to themselves, while maintaining similar efficiency to the counterpart of translation pieces.

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Vorheriges Kapitel Select the Best Translation from Different Systems Without Reference

Nächstes Kapitel Cross Aggregation of Multi-head Attention for Neural Machine Translation

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Titel: Word Position Aware Translation Memory for Neural Machine Translation
verfasst von: Qiuxiang He
Guoping Huang
Lemao Liu
Li Li
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-32232-8

Electronic ISBN: 978-3-030-32233-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32233-5_29

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