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Machine Translation: 20th China Conference, CCMT 2024, Xiamen, China, November 810, 2024, Proceedings [Mīkstie vāki]

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  • Formāts: Paperback / softback, 176 pages, height x width: 235x155 mm, 38 Illustrations, color; 16 Illustrations, black and white; XIV, 176 p. 54 illus., 38 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2365
  • Izdošanas datums: 20-Feb-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819622913
  • ISBN-13: 9789819622917
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Machine Translation: 20th China Conference, CCMT 2024, Xiamen, China, November 810, 2024, ProceedingsPalielināt
  • Formāts: Paperback / softback, 176 pages, height x width: 235x155 mm, 38 Illustrations, color; 16 Illustrations, black and white; XIV, 176 p. 54 illus., 38 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2365
  • Izdošanas datums: 20-Feb-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819622913
  • ISBN-13: 9789819622917
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819622917.html
Keywords:
This book constitutes the refereed proceedings of the 20th China Conference on Machine Translation, CCMT 2024, which took place in Xiamen, China, during November 810, 2024.





The 13 full papers included in this book were carefully reviewed and selected from 52 submissions. They were organized in topical sections as follows: robustness and efficiency of translation models; low-resource machine translation; quality estimation; large language modes for machine translation; multi-modal translation; and machine translation evaluation.
.- Robustness and Efficiency of Translation Models.

.- A Data-Efficient Nearest-Neighbor Language Model via Lightweight Nets.

.- Extend Adversarial Policy Against Neural Machine Translation via Unknown
Token.

.- Low-resource Machine Translation.

.- Evaluating the Translation Performance of Multilingual Large Language
Models: a Case Study on Southeast Asian Language.

.- Quality Estimation.

.- Critical Error Detection based on Anchors Test.

.- Large Language Modes for Machine Translation.

.- Enhancing Machine Translation Across Multiple Domains and Languages with
Large Language Models.

.- Incorporating Terminology Knowledge into Large Language Model for
Domain-specific Machine Translation.

.- Multi-modal Translation.

.- Joint Multi-modal Modeling for Speech-to-Text Translation as Multilingual
Neural Machine Translation.

.- Machine Translation Evaluation.

.- CCMT2024 Tibetan-Chinese Machine Translation Evaluation Technical Report.

.- HW-TSCs Submission to the CCMT 2024 Machine Translation Task.

.- ISTICs Neural Machine Translation Systems for CCMT
2024.

.- Lan-Bridges Submission to CCMT 2024 Translation Evaluation Task.

.- Technical Report of OPPOs Machine Translation Systems for CCMT
2024.

.- Xihongs Submission to CCMT 2024: Human-in-the-Loop Data Augmentation for
Low-Resource Tibetan-Chinese NMT.