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2018 | OriginalPaper | Buchkapitel

Improving Chinese-English Neural Machine Translation with Detected Usages of Function Words

verfasst von : Kunli Zhang, Hongfei Xu, Deyi Xiong, Qiuhui Liu, Hongying Zan

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

One of difficulties in Chinese-English machine translation is that the grammatical meaning expressed by morphology or syntax in target translations is usually determined by Chinese function words or word order. In order to address this issue, we develop classifiers to automatically detect usages of common Chinese function words based on Chinese Function usage Knowledge Base (CFKB) and initially propose a function word usage embedding model to incorporate detection results into neural machine translation (NMT). Experiments on the NIST Chinese-English translation task demonstrate that the proposed method can obtain significant improvements on the quality of both translation and word alignment over the NMT baseline.

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Fußnoten
1
Extracted from LDC2002E18, LDC2003E07, LDC2003E14, Hansards portion of LDC2004T07, LDC2004T08 and LDC2005T06.
 
2
We observe that the “concat” and “usage” method perform better than the “part” method in terms of both translation and alignment quality in “DE”’s experiment. Therefore we don’t have the experiments of “part” method with the other function words.
 
3
After the Chinese sentence is its reference translation, “BASE” means baseline model, “FNMT” means model with function word usages, text after “/” is the usage of Chinese function words.
 
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Metadaten
Titel
Improving Chinese-English Neural Machine Translation with Detected Usages of Function Words
verfasst von
Kunli Zhang
Hongfei Xu
Deyi Xiong
Qiuhui Liu
Hongying Zan
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-73618-1_64