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Machine Translation

Overview of attention for book
Machine Translation
Springer Nature Singapore

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 PEACook: Post-editing Advancement Cookbook
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    Chapter 2 Hot-Start Transfer Learning Combined with Approximate Distillation for Mongolian-Chinese Neural Machine Translation
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    Chapter 3 Review-Based Curriculum Learning for Neural Machine Translation
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    Chapter 4 Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages
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    Chapter 5 Target-Side Language Model for Reference-Free Machine Translation Evaluation
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    Chapter 6 Life Is Short, Train It Less: Neural Machine Tibetan-Chinese Translation Based on mRASP and Dataset Enhancement
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    Chapter 7 Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples
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    Chapter 8 Dynamic Mask Curriculum Learning for Non-Autoregressive Neural Machine Translation
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    Chapter 9 Dynamic Fusion Nearest Neighbor Machine Translation via Dempster-Shafer Theory
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    Chapter 10 A Multi-tasking and Multi-stage Chinese Minority Pre-trained Language Model
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    Chapter 11 An Improved Multi-task Approach to Pre-trained Model Based MT Quality Estimation
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    Chapter 13 CCMT 2022 Translation Quality Estimation Task
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    Chapter 14 Effective Data Augmentation Methods for CCMT 2022
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    Chapter 15 NJUNLP’s Submission for CCMT 2022 Quality Estimation Task
  16. Altmetric Badge
    Chapter 16 ISTIC’s Thai-to-Chinese Neural Machine Translation System for CCMT’ 2022
Attention for Chapter 14: Effective Data Augmentation Methods for CCMT 2022
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Chapter title
Effective Data Augmentation Methods for CCMT 2022
Chapter number 14
Book title
Machine Translation
Published by
Springer, Singapore, December 2022
DOI 10.1007/978-981-19-7960-6_14
Book ISBNs
978-9-81-197959-0, 978-9-81-197960-6
Authors

Wang, Jing, Yang, Lina

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The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Readers by discipline Count As %
Unspecified 1 100%

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