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Titlebook: Machine Translation; 19th China Conferenc Yang Feng,Chong Feng Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s),

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樓主
發(fā)表于 2025-3-21 17:24:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Translation
副標(biāo)題19th China Conferenc
編輯Yang Feng,Chong Feng
視頻videohttp://file.papertrans.cn/621/620779/620779.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Machine Translation; 19th China Conferenc Yang Feng,Chong Feng Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s),
描述This book constitutes the refereed proceedings of the 19th China Conference on Machine Translation, CCMT 2023, held in Jinan, China, during October 19–21, 2023.?.The 8 full papers and 3 short papers included in this book were carefully reviewed and selected from 71 submissions. They focus on machine translation; improvement of translation models and systems; translation quality estimation; document-level machine translation; low-resource machine translation..
出版日期Conference proceedings 2023
關(guān)鍵詞machine translation; neural machine translation; translation quality estimation; document-level machine
版次1
doihttps://doi.org/10.1007/978-981-99-7894-6
isbn_softcover978-981-99-7893-9
isbn_ebook978-981-99-7894-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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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沙發(fā)
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板凳
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,HIT-MI &T Lab’s Submission to?CCMT 2023 Automatic Post-editing Task,ore different techniques to create synthetic data, including domain selection, forward translation and data augmentation via large language model. Multi-model ensemble is also adopted in our final system. The experiment results on the development set demonstrate the effectiveness of our proposed method.
地板
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,A Novel Dataset and?Benchmark Analysis on?Document Image Translation, . and . for better DIT, has been proposed. Furthermore, benchmark evaluations and detailed analysis based on this framework have been conducted. The evaluations and analysis results demonstrate that the dataset is very practical and can facilitate full-stack analysis and long-term research on DIT.
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發(fā)表于 2025-3-23 00:04:05 | 只看該作者
1865-0929 October 19–21, 2023.?.The 8 full papers and 3 short papers included in this book were carefully reviewed and selected from 71 submissions. They focus on machine translation; improvement of translation models and systems; translation quality estimation; document-level machine translation; low-resourc
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發(fā)表于 2025-3-23 06:58:51 | 只看該作者
,ISTIC’s Neural Machine Translation Systems for?CCMT’ 2023,yghur.Chinese). Context-aware systems and a multilingual system are built for two tasks respectively. The paper mainly illuminates our systems’ architecture based on Transformer, data preprocessing methods and some strategies adopted in these systems. In addition, the paper evaluates the systems’ performance under different methods.
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