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  1. Machine Translation
    18th China Conference, CCMT 2022, Lhasa, China, August 6-10, 2022, revised selected papers
    Contributor: Pino, Juan (HerausgeberIn); Xiao, Tong (HerausgeberIn)
    Published: [2022]; ©2022
    Publisher:  Springer, Singapore

    Intro -- Preface -- Organization -- Contents -- PEACook: Post-editing Advancement Cookbook -- 1 Introduction -- 2 Related Work -- 2.1 APE Problem and APE Metrics -- 2.2 APE Baselines -- 3 PEACook Corpus -- 3.1 PEACook Corpus Details -- 4 Baseline... more

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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Potsdamer Straße
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    Universitätsbibliothek Clausthal
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    Location:
    Hochschulbibliothek Friedensau
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    Online-Ressource
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    Location:
    Hochschule Schmalkalden, Cellarius Bibliothek
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    Intro -- Preface -- Organization -- Contents -- PEACook: Post-editing Advancement Cookbook -- 1 Introduction -- 2 Related Work -- 2.1 APE Problem and APE Metrics -- 2.2 APE Baselines -- 3 PEACook Corpus -- 3.1 PEACook Corpus Details -- 4 Baseline Model Experiments -- 4.1 Pre-training AR-APE Model -- 4.2 Fine-Tuning AR-APE Model -- 4.3 Pre-training NAR-APE Model -- 4.4 Fine-Tuning NAR-APE Model -- 5 Conclusion -- References -- Hot-Start Transfer Learning Combined with Approximate Distillation for Mongolian-Chinese Neural Machine Translation -- 1 Introduction -- 2 Background -- 2.1 NMT -- 2.2 Transfer Learning -- 2.3 Pre-train Techniques -- 3 Methods -- 3.1 Word Alignment Under Hot-Start -- 3.2 Approximate Distillation -- 4 Experiment -- 4.1 Settings -- 4.2 Results and Analysis -- 4.3 Ablation Test -- 4.4 Case Analysis -- 5 Conclusion -- References -- Review-Based Curriculum Learning for Neural Machine Translation -- 1 Introduction -- 2 Related Work -- 3 Review-Based Curriculum Learning -- 3.1 Time-Based Review Method -- 3.2 Master-Based Review Method -- 3.3 General Domain Enhanced Training -- 4 Experiment -- 4.1 Data and Setup -- 4.2 Main Results -- 5 Analysis -- 5.1 Effect of Mixed Fine Tuning -- 5.2 Low-Resource Scenario -- 5.3 Data Sharding -- 5.4 Training Efficiency -- 6 Conclusion -- References -- Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages -- 1 Introduction -- 2 Dataset -- 3 System Overview -- 3.1 Back-Translation -- 3.2 Alternated Training -- 3.3 Ensemble -- 4 Experiments -- 4.1 Mongolian Chinese -- 4.2 TibetanChinese -- 4.3 UyghurChinese -- 5 Analysis -- 5.1 The Effect of Different Back-Translation Methods -- 5.2 The Impact of Sentence Segmentation on the Translation Quality of Machine Translation -- 5.3 Analysis of BLEU Scores of MongolianChinese Machine Translation on the Development Set.

     
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  2. Machine Translation
    18th China Conference, CCMT 2022, Lhasa, China, August 6-10, 2022, revised selected papers
    Contributor: Pino, Juan (HerausgeberIn); Xiao, Tong (HerausgeberIn)
    Published: [2022]; ©2022
    Publisher:  Springer, Singapore

    Intro -- Preface -- Organization -- Contents -- PEACook: Post-editing Advancement Cookbook -- 1 Introduction -- 2 Related Work -- 2.1 APE Problem and APE Metrics -- 2.2 APE Baselines -- 3 PEACook Corpus -- 3.1 PEACook Corpus Details -- 4 Baseline... more

    Access:
    Aggregator (lizenzpflichtig)

    Location:
    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

     

    Intro -- Preface -- Organization -- Contents -- PEACook: Post-editing Advancement Cookbook -- 1 Introduction -- 2 Related Work -- 2.1 APE Problem and APE Metrics -- 2.2 APE Baselines -- 3 PEACook Corpus -- 3.1 PEACook Corpus Details -- 4 Baseline Model Experiments -- 4.1 Pre-training AR-APE Model -- 4.2 Fine-Tuning AR-APE Model -- 4.3 Pre-training NAR-APE Model -- 4.4 Fine-Tuning NAR-APE Model -- 5 Conclusion -- References -- Hot-Start Transfer Learning Combined with Approximate Distillation for Mongolian-Chinese Neural Machine Translation -- 1 Introduction -- 2 Background -- 2.1 NMT -- 2.2 Transfer Learning -- 2.3 Pre-train Techniques -- 3 Methods -- 3.1 Word Alignment Under Hot-Start -- 3.2 Approximate Distillation -- 4 Experiment -- 4.1 Settings -- 4.2 Results and Analysis -- 4.3 Ablation Test -- 4.4 Case Analysis -- 5 Conclusion -- References -- Review-Based Curriculum Learning for Neural Machine Translation -- 1 Introduction -- 2 Related Work -- 3 Review-Based Curriculum Learning -- 3.1 Time-Based Review Method -- 3.2 Master-Based Review Method -- 3.3 General Domain Enhanced Training -- 4 Experiment -- 4.1 Data and Setup -- 4.2 Main Results -- 5 Analysis -- 5.1 Effect of Mixed Fine Tuning -- 5.2 Low-Resource Scenario -- 5.3 Data Sharding -- 5.4 Training Efficiency -- 6 Conclusion -- References -- Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages -- 1 Introduction -- 2 Dataset -- 3 System Overview -- 3.1 Back-Translation -- 3.2 Alternated Training -- 3.3 Ensemble -- 4 Experiments -- 4.1 Mongolian Chinese -- 4.2 TibetanChinese -- 4.3 UyghurChinese -- 5 Analysis -- 5.1 The Effect of Different Back-Translation Methods -- 5.2 The Impact of Sentence Segmentation on the Translation Quality of Machine Translation -- 5.3 Analysis of BLEU Scores of MongolianChinese Machine Translation on the Development Set.

     
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  3. Machine Translation
    18th China Conference, CCMT 2022, Lhasa, China, August 6-10, 2022, revised selected papers
    Contributor: Pino, Juan (HerausgeberIn); Xiao, Tong (HerausgeberIn)
    Published: [2022]
    Publisher:  Springer, Singapore

    This book constitutes the refereed proceedings of the 18th China Conference onMachine 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... more

    Location:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Signature:
    RS 7445(1671)
    Inter-library loan:
    No loan of volumes, only paper copies will be sent

     

    This book constitutes the refereed proceedings of the 18th China Conference onMachine 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

     
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    Content information
    Source: Union catalogues
    Contributor: Pino, Juan (HerausgeberIn); Xiao, Tong (HerausgeberIn)
    Language: English
    Media type: Conference proceedings
    Format: Print
    ISBN: 9789811979590
    Corporations / Congresses: CCMT, 18. (2022, Lhasa)
    Series: Communications in Computer and Information Science ; 1671
    Subjects: COMPUTERS / Data Processing / General; COMPUTERS / Data Processing / Speech & Audio Processing; COMPUTERS / Database Management / General; COMPUTERS / Information Theory; COMPUTERS / Programming / General; Coding theory & cryptology; Computer programming / software development; Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften; Databases; Datenbanken; Informationstheorie; Kodierungstheorie und Verschlüsselung (Kryptologie); Natural language & machine translation; Natürliche Sprachen und maschinelle Übersetzung; Society & social sciences; Theoretische Informatik
    Scope: xiv, 160 Seiten, Illustrationen, Diagramme
    Notes:

    Interessenniveau: 06, Professional and scholarly: For an expert adult audience, including academic research. (06)

    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 Dempster-Shafer 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.- NJUNLP's Submission for CCMT 2022 Quality Estimation Task.- ISTIC's Thai-to-Chinese Neural Machine Translation System for CCMT' 2022.