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Machine Translation: 19th China Conference, CCMT 2023, Jinan, China, October 1921, 2023, Proceedings 1st ed. 2023 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 130 pages, height x width: 235x155 mm, weight: 231 g, 40 Illustrations, color; 29 Illustrations, black and white; XIV, 130 p. 69 illus., 40 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1922
  • Izdošanas datums: 01-Nov-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819978939
  • ISBN-13: 9789819978939
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Machine Translation: 19th China Conference, CCMT 2023, Jinan, China, October 1921, 2023, Proceedings 1st ed. 2023Palielināt
  • Formāts: Paperback / softback, 130 pages, height x width: 235x155 mm, weight: 231 g, 40 Illustrations, color; 29 Illustrations, black and white; XIV, 130 p. 69 illus., 40 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1922
  • Izdošanas datums: 01-Nov-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819978939
  • ISBN-13: 9789819978939
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819978939.html
Keywords:
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.
Transn's submission for CCMT 2023 Quality Estimation Task.- HW-TSC's
Neural Machine Translation System for CCMT 2023.- CCMT2023 Machine
Translation Evaluation Technical Report.- Korean-Chinese Machine Translation
Method Based on Independent Language Features.- NJUNLP's Submission for CCMT
2023 Quality Estimation Task.- HIT-MI&T Lab's Submission to CCMT 2023
Automatic Post-Editing Task.- A k-Nearest Neighbor Approach for
Domain-Specific Translation Quality Estimation.- WSA: A Unified Framework for
Word and Sentence Autocompletion in Interactive Machine
Translation.- ISTIC's Neural Machine Translation Systems for CCMT'2023.- A
Novel Dataset and Benchmark Analysis on Document Image Translation.- Joint
Contrastive Learning for Factual Consistency Evaluation of Cross-Lingual
Abstract Summarization.