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2019 | OriginalPaper | Chapter

Improved Neural Machine Translation with POS-Tagging Through Joint Decoding

Authors : Xiaocheng Feng, Zhangyin Feng, Wanlong Zhao, Nan Zou, Bing Qin, Ting Liu

Published in: Artificial Intelligence for Communications and Networks

Publisher: Springer International Publishing

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Abstract

In this paper, we improve the performance of neural machine translation (NMT) with shallow syntax (e.g., POS tag) of target language, which has better accuracy and latency than deep syntax such as dependency parsing. We present three NMT decoding models (independent decoder, gates shared decoder and fully shared decoder) to jointly predict target word and POS tag sequences. Experiments on Chinese-English and German-English translation tasks show that the fully shared decoder can acquire the best performance, which increases the BLEU score by 1.4 and 2.25 points respectively compared with the attention-based NMT model.

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Footnotes
1
Six combinations (shared gates / independent gates): {[input / forget, output], [input, forget / output], [input,output / forget], [forget / input, output], [output / input, forget], [forget, output / input]}.
 
2
The codes are implemented with Pytorch, which we plan to release to the community.
 
3
The corpora includes LDC2002E18, LDC2003E07, LDC2003E14, the Hansards portion of LDC2004T08, and LDC2005T06.
 
5
The value of kappa is 0.65 in 1–5 scale on two dimensions.
 
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Metadata
Title
Improved Neural Machine Translation with POS-Tagging Through Joint Decoding
Authors
Xiaocheng Feng
Zhangyin Feng
Wanlong Zhao
Nan Zou
Bing Qin
Ting Liu
Copyright Year
2019
Book
Artificial Intelligence for Communications and Networks

Print ISBN: 978-3-030-22967-2

Electronic ISBN: 978-3-030-22968-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-22968-9_14

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