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Erschienen in:
Enabling Medical Translation for Low-Resource Languages Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Shonosuke Ishiwatari, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: 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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Vorheriges Kapitel Forest to String Based Statistical Machine Translation with Hybrid Word Alignments
Nächstes Kapitel Fast-Syntax-Matching-Based Japanese-Chinese Limited Machine Translation
Fußnoten
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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Metadaten
Titel
Instant Translation Model Adaptation by Translating Unseen Words in Continuous Vector Space
verfasst von
Shonosuke Ishiwatari
Naoki Yoshinaga
Masashi Toyoda
Masaru Kitsuregawa
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

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

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

Copyright-Jahr: 2018

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

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