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Titlebook: Machine Translation; 10th China Workshop, Xiaodong Shi,Yidong Chen Conference proceedings 2014 Springer-Verlag Berlin Heidelberg 2014 crowd

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發(fā)表于 2025-3-21 17:12:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Translation
副標(biāo)題10th China Workshop,
編輯Xiaodong Shi,Yidong Chen
視頻videohttp://file.papertrans.cn/621/620776/620776.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Machine Translation; 10th China Workshop, Xiaodong Shi,Yidong Chen Conference proceedings 2014 Springer-Verlag Berlin Heidelberg 2014 crowd
描述This book constitutes the refereed proceedings of the 10th China Workshop on Machine Translation, CWMT 2014, held in Macau, China, in November 2014. The 10 revised full English papers presented were carefully reviewed and selected from 15 submissions of English papers. The papers cover the following topics: machine translation; data selection; word segmentation; entity recognition; MT evaluation.
出版日期Conference proceedings 2014
關(guān)鍵詞crowdsourcing; kernel ridge regression; language model; machine translation; word segmentation; algorithm
版次1
doihttps://doi.org/10.1007/978-3-662-45701-6
isbn_softcover978-3-662-45700-9
isbn_ebook978-3-662-45701-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer-Verlag Berlin Heidelberg 2014
The information of publication is updating

書目名稱Machine Translation影響因子(影響力)




書目名稱Machine Translation影響因子(影響力)學(xué)科排名




書目名稱Machine Translation網(wǎng)絡(luò)公開度




書目名稱Machine Translation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Translation被引頻次




書目名稱Machine Translation被引頻次學(xué)科排名




書目名稱Machine Translation年度引用




書目名稱Machine Translation年度引用學(xué)科排名




書目名稱Machine Translation讀者反饋




書目名稱Machine Translation讀者反饋學(xué)科排名




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Effective Hypotheses Re-ranking Model in Statistical Machine Translation,ain UM-Corpus and evaluated on IWSLT Chinese-English TED Talk data under the configurations of . translation and . translation respectively. Empirical results real that sentence level translation model outperforms the document level and the baseline system.
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Recognizing and Reordering the Translation Units in a Long NP for Chinese-English Patent Machine Trinese NP, we built some formalized rules to recognize the boundaries of translation units using the boundary words to identify what to reorder. At last, we used a rule-based MT system to test our work, and the experimental results showed that our rule-based method and strategy were very efficient.
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1865-0929 na Workshop on Machine Translation, CWMT 2014, held in Macau, China, in November 2014. The 10 revised full English papers presented were carefully reviewed and selected from 15 submissions of English papers. The papers cover the following topics: machine translation; data selection; word segmentatio
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