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Titlebook: Machine Translation; 17th China Conferenc Jinsong Su,Rico Sennrich Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 arti

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發(fā)表于 2025-3-21 16:55:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Machine Translation
副標(biāo)題17th China Conferenc
編輯Jinsong Su,Rico Sennrich
視頻videohttp://file.papertrans.cn/621/620772/620772.mp4
叢書(shū)名稱(chēng)Communications in Computer and Information Science
圖書(shū)封面Titlebook: Machine Translation; 17th China Conferenc Jinsong Su,Rico Sennrich Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 arti
描述This book constitutes the refereed proceedings of the 17th China Conference on Machine Translation, CCMT 2020, held in Xining, China, in October 2021.?.The 10 papers presented in this volume were carefully reviewed and selected from 25 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; automata theory; communication; computational linguistics; computer aided langu
版次1
doihttps://doi.org/10.1007/978-981-16-7512-6
isbn_softcover978-981-16-7511-9
isbn_ebook978-981-16-7512-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

書(shū)目名稱(chēng)Machine Translation影響因子(影響力)




書(shū)目名稱(chēng)Machine Translation影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Machine Translation網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Machine Translation網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Machine Translation被引頻次




書(shū)目名稱(chēng)Machine Translation被引頻次學(xué)科排名




書(shū)目名稱(chēng)Machine Translation年度引用




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書(shū)目名稱(chēng)Machine Translation讀者反饋




書(shū)目名稱(chēng)Machine Translation讀者反饋學(xué)科排名




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Semantic-Aware Deep Neural Attention Network for Machine Translation Detection,er for monolingual detection and further explores the semantic consistency relationship for bilingual detection. The experimental results on the Chinese-English machine translation detection task show that our models achieve 83.12% . in the monolingual detection and 85.53% . in the bilingual detecti
地板
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Routing Based Context Selection for Document-Level Neural Machine Translation,l structure information more effectively. At the same time, this structured information selection mechanism will also alleviate the possible problems caused by long-distance encoding. Experimental results show that our method is 2.91 BLEU higher than the Transformer model on the public dataset of ZH
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Nier Wu,Hongxu Hou,Xiaoning Jia,Xin Chang,Haoran Li
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Nier Wu,Hongxu Hou,Haoran Li,Xin Chang,Xiaoning Jia
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Yiqi Tong,Yidong Chen,Guocheng Zhang,Jiangbin Zheng,Hongkang Zhu,Xiaodong Shi
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