08222nam 2200481 450 99650356610331620230417074904.0981-19-7960-X(MiAaPQ)EBC7153756(Au-PeEL)EBL7153756(CKB)25616982400041(EXLCZ)992561698240004120230417d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine translation 18th China conference, CCMT 2022, Lhasa, China, August 6-10, 2022 : revised selected papers /Tong Xiao and Juan PinoSingapore :Springer,[2022]©20221 online resource (175 pages)Communications in Computer and Information SciencePrint version: Xiao, Tong Machine Translation Singapore : Springer,c2023 9789811979590 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.6 Conclusion -- References -- Target-Side Language Model for Reference-Free Machine Translation Evaluation -- 1 Introduction -- 2 Target-Side Language Model Metrics -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Results -- 3.3 Discussion -- 4 Conclusion -- References -- Life Is Short, Train It Less: Neural Machine Tibetan-Chinese Translation Based on mRASP and Dataset Enhancement -- 1 Introduction -- 2 Prerequisite -- 2.1 Neural Machine Translation with mRASP -- 2.2 Diversification Method -- 2.3 Curvature -- 3 Methodology -- 3.1 Overall Structure -- 3.2 Curvature Based Checkpoint Hijack -- 4 Experiments -- 4.1 Dataset Description and Finetune Parameters -- 4.2 Experiment Result -- 5 Conclusion -- References -- Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples -- 1 Introduction -- 2 Background and Related Work -- 2.1 Neural Machine Translation -- 2.2 Adversarial Example, Adversarial Attack and Adversarial Training in NLP -- 2.3 Genetic Algorithm-Based Adversarial Attack -- 2.4 Gradient-Based Adversarial Attack -- 3 Adversarial Examples Based on Reinforcement Learning -- 3.1 Reinforcement Learning -- 3.2 Environment -- 3.3 Agent -- 4 Experiment -- 4.1 Data Preprocessing -- 4.2 NMT Model -- 4.3 Evaluating Indicator -- 4.4 Adversarial Attack Results and Analysis -- 4.5 Adversarial Training Results and Analysis -- 4.6 Ablation Study -- 5 Conclusion -- References -- Dynamic Mask Curriculum Learning for Non-Autoregressive Neural Machine Translation -- 1 Introduction -- 2 Background -- 2.1 Non-autoregressive Neural Machine Translation -- 2.2 Curriculum Learning -- 3 Method -- 3.1 Model -- 3.2 Dynamic Mask Curriculum Learning -- 3.3 Train and Inference -- 4 Experiment -- 4.1 Data Preparation -- 4.2 Configuration -- 4.3 Baseline -- 4.4 Results -- 5 Analysis -- 5.1 Mask Strategy -- 5.2 Method Generality.6 Conclusion -- References -- Dynamic Fusion Nearest Neighbor Machine Translation via Dempster-Shafer Theory -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Dempster-Shafer Theory -- 3.2 Label Smoothing -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Robustness -- 4.4 Case Study -- 5 Conclusion -- References -- A Multi-tasking and Multi-stage Chinese Minority Pre-trained Language Model -- 1 Introduction -- 2 Related Work -- 2.1 Pre-trained Language Model -- 2.2 Multilingual Model -- 2.3 Chinese Minority Languages -- 3 Main Methods -- 3.1 Model Architecture -- 3.2 Multi-tasking Multi-stage Pre-training -- 3.3 Model Parameter Details -- 3.4 Model Setting Details -- 4 Experiments -- 4.1 Main Results -- 4.2 Case Study -- 5 Conclusion -- References -- An Improved Multi-task Approach to Pre-trained Model Based MT Quality Estimation -- 1 Introduction -- 2 Related Works -- 3 PE Based Multi-task Learning for Sentence Level QE -- 3.1 Multi-task Learning Framework for QE -- 3.2 PE Based Multi-task Learning QE -- 3.3 Multi-model Ensemble -- 4 Experiments -- 4.1 Dataset -- 4.2 Model Training and Evaluation Metric -- 4.3 Experimental Results and Analysis -- 4.4 Ablation Study -- 5 Conclusion -- References -- Optimizing Deep Transformers for Chinese-Thai Low-Resource Translation -- 1 Introduction -- 2 Background -- 2.1 Transformer -- 2.2 Low-Resource NMT -- 2.3 Parameter Initialization for Deep Transformers -- 2.4 Deep Transformers for Low-Resource Tasks -- 3 Our Work -- 3.1 Data Processing -- 3.2 Exploration of Training Settings -- 3.3 Deep Transformers for Low-Resource Machine Translation -- 4 Related Work -- 5 Conclusion -- References -- CCMT 2022 Translation Quality Estimation Task -- 1 Introduction -- 2 Estimation System -- 3 Data -- 4 Method -- 4.1 System Training -- 4.2 System Test -- 5 Experiment -- 5.1 System Environment.5.2 Experiment Settings -- 5.3 Experiment Result -- 6 Conclusion -- References -- Effective Data Augmentation Methods for CCMT 2022 -- 1 Introduction -- 2 System Architecture -- 3 Methods -- 3.1 Data Augmentation -- 3.2 CE Task and EC Task -- 3.3 CThai Task and ThaiC Task -- 4 Experiments -- 4.1 System Settings -- 4.2 Data Pre-processing -- 4.3 Experimental Results -- 5 Conclusion -- References -- NJUNLP's Submission for CCMT 2022 Quality Estimation Task -- 1 Introduction -- 2 Methods -- 2.1 Existing Methods -- 2.2 Proposed Methods -- 3 Experiments -- 3.1 Dataset -- 3.2 Settings -- 3.3 Single Model Results -- 3.4 Ensemble -- 3.5 Analysis -- 4 Conclusion -- References -- ISTIC's Thai-to-Chinese Neural Machine Translation System for CCMT' 2022 -- 1 Introduction -- 2 System Architecture -- 2.1 Baseline System -- 2.2 Our System -- 3 Methods -- 3.1 Back Translation -- 3.2 Add External Data -- 3.3 Model Averaging -- 3.4 Model Ensemble Strategy -- 4 Experiments -- 4.1 System Settings -- 4.2 Data Preprocessing -- 4.3 Experimental Results -- 4.4 Conclusion -- References -- Author Index.Communications in computer and information science.Chinese languageMachine translatingMachine translatingCongressesChinese languageMachine translating.Machine translating495.10285Xiao Tong1271989Pino JuanMiAaPQMiAaPQMiAaPQBOOK996503566103316Machine translation3088625UNISA01751nac# 22002771i 450 UON0017560420231205103111.57920030730f |0itac50 baES|||| |||||b||||||||||Breve Biblioteca de RespuestaBarcelonaBarral001UON004961082001 Mario Vargas Llosala invención de una realidadJose Miguel Oviedo205 2a ed210 BarcelonaBarral1977215 392 p.20 cm1001UON004972502001 ˆEl ‰combate imaginariolas cartas de batalla de Joanot MartorellJoanot Martorellpor Martín de Riquer y Mario Vargas Llosa210 BarcelonaBarral1972215 143 p.20 cm.36001UON002192322001 Introducción a los vasos órficosJosè Lezama Lima210 BarcelonaBarral Editores1971. 272 p. ; 20 cm16001UON003392012001 ˆLos ‰años sin excusa /Carlos Barral210 BarcelonaBarral editores1978215 311 p.20 cm.001UON002197662001 Fundadores de la nueva poesia latinoamericanaVallejo, Huidobro, Borges, Neruda, PazSaul Yurkievich210 BarcelonaBarral Editores1971. 236 p. ; 20 cm.001UON002271812001 Garcia Marquezhistoria de un deicidioMario Vargas Llosa210 2ª edicíon. BarcelonaBarral1971. 667 p. ; 20 cm.001UON002195782001 Mario Vargas Llosala invención de una realidadJose Miguel Oviedo210 BarcelonaBarral1970215 272 p.20 cmESBarcelonaUONL003004BarralUONV268573650ITSOL20240220RICAUON00175604Breve biblioteca de Respuesta935986UNIOR