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Record Nr. |
UNISA996503566103316 |
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Autore |
Xiao Tong |
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Titolo |
Machine translation : 18th China conference, CCMT 2022, Lhasa, China, August 6-10, 2022 : revised selected papers / / Tong Xiao and Juan Pino |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (175 pages) |
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Collana |
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Communications in Computer and Information Science |
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Disciplina |
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Soggetti |
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Chinese language - Machine translating |
Machine translating |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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 -- |
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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. |
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2. |
Record Nr. |
UNINA9910794517603321 |
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Autore |
Bernhart Walter |
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Titolo |
Arts of Incompletion : Fragments in Words and Music |
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Pubbl/distr/stampa |
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Boston : , : BRILL, , 2021 |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (304 pages) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Music and literature |
Fragmentation (Philosophy) in music |
Fragmentation (Philosophy) in literature |
Music in literature |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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"Incompletion is an essential condition of cultural history, and particularly the idea of the fragment became a central element of Romantic art. Through its resistance to classicist ideals it continued being of high relevance to the various strands of modernist and contemporary aesthetics. The fourteen essays in this volume, based on the 2017 Stockholm conference of the International Association for Word and Music Studies (WMA), for the first time address incompletion in a wide range of literary and musical texts, from Baudelaire and Flaubert through Tolstoy and Henry James to Bachmann, Jelinek and Janet Frame, from Nietzsche and Chopin through Russolo and Puccini to Rihm and Kurtàg. Two further essays deal with topical general issues in the field of word and music studies"-- |
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