A novel word reordering method for statistical machine translation

Zang, Shuo; Zhao, Hai; Wu, Chunyang; Wang, Rui

doi:10.1109/fskd.2015.7382052

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 2015

DOI: 10.1109/fskd.2015.7382052

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A novel word reordering method for statistical machine translation

Shuo Zang

Hai Zhao

Chunyang Wu

et al.

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Cited by 2 publications

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Learning Better Classification-Based Reordering Model for Phrase-Based Translation

Li¹,

Xiao²,

Zhu³

2017

2017 International Conference on Computer Network, Electronic and Automation (ICCNEA)

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Reordering is of a challenging issue in phrase-based statistical machine translation systems. This paper proposed three techniques to optimize classification-based reordering models for phrase-based translation under the bracket transduction grammar framework. First, a forced decoding technique is adopted to learn reordering samples for maximum entropy model training. Secondly, additional features are learned from the context of two consecutive phrases to enhance the prediction ability of the reordering classifier. Thirdly, the reordering model score is integrated as two feature functions (STRAIGHT and INVERTED) into the log-linear model to improve its discriminative ability. Experimental result demonstrates significant improvements over the baseline in two translation tasks such as Chinese to English and Chinese to Japanese translation.

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Learning Better Classification-Based Reordering Model for Phrase-Based Translation

Li¹,

Xiao²,

Zhu³

2017

2017 International Conference on Computer Network, Electronic and Automation (ICCNEA)

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show abstract

Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue

Park¹,

Zhao²

2019

Preprint

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Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more then fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentence to Chinese characters and then train machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models.

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A novel word reordering method for statistical machine translation

Cited by 2 publications

References 20 publications

Learning Better Classification-Based Reordering Model for Phrase-Based Translation

Learning Better Classification-Based Reordering Model for Phrase-Based Translation

Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue

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