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Enabling Medical Translation for Low-Resource Languages Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Instant Translation Model Adaptation by Translating Unseen Words in Continuous Vector Space

Authors : Shonosuke Ishiwatari, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa

Published in: Computational Linguistics and Intelligent Text Processing

Publisher: Springer International Publishing

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Abstract

In statistical machine translation (smt), differences between domains of training and test data result in poor translations. Although there have been many studies on domain adaptation of language models and translation models, most require supervised in-domain language resources such as parallel corpora for training and tuning the models. The necessity of supervised data has made such methods difficult to adapt to practical smt systems. We thus propose a novel method that adapts translation models without in-domain parallel corpora. Our method infers translation candidates of unseen words by nearest-neighbor search after projecting their vector-based semantic representations to the semantic space of the target language. In our experiment of out-of-domain translation from Japanese to English, our method improved bleu score by 0.5–1.5.

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previous chapter Forest to String Based Statistical Machine Translation with Hybrid Word Alignments
next chapter Fast-Syntax-Matching-Based Japanese-Chinese Limited Machine Translation
Footnotes
1
k was set to 10 in the experiments.
 
7
http://​dumps.​wikimedia.​org/​ (versions of Nov, 4th, 2014 (ja), Oct, 8th, 2014 (en).
 
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Metadata
Title
Instant Translation Model Adaptation by Translating Unseen Words in Continuous Vector Space
Authors
Shonosuke Ishiwatari
Naoki Yoshinaga
Masashi Toyoda
Masaru Kitsuregawa
Copyright Year
2018
Book
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75487-1_5

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