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Machine Translation: 18th China Conference, CCMT 2022, Lhasa, China, August 610, 2022, Revised Selected Papers 1st ed. 2022 [Pehme köide]

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  • Formaat: Paperback / softback, 161 pages, kõrgus x laius: 235x155 mm, kaal: 279 g, 49 Illustrations, color; 11 Illustrations, black and white; XIV, 161 p. 60 illus., 49 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1671
  • Ilmumisaeg: 09-Dec-2022
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811979596
  • ISBN-13: 9789811979590
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Machine Translation: 18th China Conference, CCMT 2022, Lhasa, China, August 610, 2022, Revised Selected Papers 1st ed. 2022Suurem pilt
  • Formaat: Paperback / softback, 161 pages, kõrgus x laius: 235x155 mm, kaal: 279 g, 49 Illustrations, color; 11 Illustrations, black and white; XIV, 161 p. 60 illus., 49 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1671
  • Ilmumisaeg: 09-Dec-2022
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811979596
  • ISBN-13: 9789811979590
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811979590.html
Märksõnad:
This book constitutes the refereed proceedings of the 18th China Conference on
Machine Translation, CCMT 2022, held in Lhasa, China, during August 6–10, 2022.

The 16 full papers were included in this book were carefully reviewed and selected from 73 submissions.
PEACook: Post-Editing Advancement Cookbook.- Hot-start Transfer Learning
combined with Approximate Distillation for Mongolian- Chinese Neural Machine
Translation.- Review-based Curriculum Learning for Neural Machine
Translation.- Multi-Strategy Enhanced Neural Machine Translation for Chinese
Minority Language.- Target-side Language Model for Reference-free Machine
Translation Evaluation.- Life Is Short, Train It Less: Neural Machine
Tibetan-Chinese Translation Based on mRASP and Dataset
Enhancement.- Improving the Robustness of Low-Resource Neural Machine
Translation with Adversarial Examples.- Dynamic Mask Curriculum Learning for
Non-Autoregressive Neural Machine Translation.- Dynamic Fusion Nearest
Neighbor Machine Translation via DempsterShafer Theory.- A Multi-tasking and
Multi-stage Chinese Minority Pre-Trained Language Model.- An improved
Multi-task Approach to Pre-trained Model Based MT
Quality Estimation.- Optimizing Deep Transformers for Chinese-Thai
Low-Resource Translation.- HW-TSC Submission for CCMT 2022 Translation
Quality Estimation Task.- Effective Data Augmentation Methods for CCMT
2022.- NJUNLPs Submission for CCMT 2022 Quality Estimation Task.- ISTICs
Thai-to-Chinese Neural Machine Translation System for CCMT 2022.