001482837 000__ 05536cam\\22005657a\4500
001482837 001__ 1482837
001482837 003__ OCoLC
001482837 005__ 20231128003353.0
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001482837 007__ cr\un\nnnunnun
001482837 008__ 231111s2023\\\\si\\\\\\o\\\\\100\0\eng\d
001482837 020__ $$a9789819978946$$q(electronic bk.)
001482837 020__ $$a9819978947$$q(electronic bk.)
001482837 0247_ $$a10.1007/978-981-99-7894-6$$2doi
001482837 035__ $$aSP(OCoLC)1407315423
001482837 040__ $$aEBLCP$$beng$$cEBLCP$$dOCLCO$$dGW5XE
001482837 049__ $$aISEA
001482837 050_4 $$aP308
001482837 08204 $$a418/.020285$$223/eng/20231113
001482837 1112_ $$aCCMT (Conference)$$n(19th :$$d2023 :$$cJinan Shi, China)
001482837 24510 $$aMachine translation :$$b19th China Conference, CCMT 2023, Jinan, China, October 19-21, 2023, Proceedings /$$cYang Feng, Chong Feng, editors.
001482837 2463_ $$aCCMT 2023
001482837 260__ $$aSingapore :$$bSpringer,$$c2023.
001482837 300__ $$a1 online resource (143 p.).
001482837 4901_ $$aCommunications in Computer and Information Science ;$$v1922
001482837 500__ $$a3.4 Results After Integrating Domain Transfer Method
001482837 500__ $$aIncludes author index.
001482837 5050_ $$aIntro -- Preface -- Organization -- Contents -- Transn's Submission for CCMT 2023 Quality Estimation Task -- 1 Introduction -- 2 Related Work -- 3 Feature-Enhanced Estimator for Sentence-Level QE -- 3.1 Model Architecture -- 3.2 Pretraining Corpus Generation -- 3.3 Model Ensemble -- 4 Experiments -- 4.1 Datasets -- 4.2 Training and Evaluation -- 4.3 Results and Analysis -- 4.4 Model Ensemble -- 5 Conclusion -- References -- HW-TSC's Neural Machine Translation System for CCMT 2023 -- 1 Introduction -- 2 Dataset -- 2.1 Data Size -- 2.2 Data Pre-processing -- 3 System Overview
001482837 5058_ $$a3.1 Bilingual System -- 3.2 Low-Resource System -- 3.3 Multilingual System -- 3.4 Zero-Referencing System -- 4 Method -- 4.1 Regularized Dropout -- 4.2 Bidirectional Training -- 4.3 Data Diversification -- 4.4 Forward Translation -- 4.5 Back-Translation -- 4.6 Alternated Training -- 4.7 Curriculum Learning -- 4.8 Transductive Ensemble Learning -- 5 Experiments -- 5.1 Bilingual System Evaluation Results -- 5.2 Low-Resource System Evaluation Results -- 5.3 Multilingual System Evaluation Results -- 5.4 Zero-Referencing System Evaluation Results -- 6 Conclusion -- References
001482837 5058_ $$aCCMT2023 Tibetan-Chinese Machine Translation Evaluation Technical Report -- 1 Introduction -- 2 Data Processing -- 2.1 Data -- 2.2 Data Preprocessing -- 3 Model -- 3.1 Model Select -- 3.2 Model Ensemble -- 3.3 Iterative Fine-Tuning -- 4 Experiment -- 4.1 Experimental Environment -- 4.2 Experimental Setup -- 5 Results and Analysis -- 6 Summary -- References -- Korean-Chinese Machine Translation Method Based on Independent Language Features -- 1 Introduction -- 2 Related Work -- 2.1 Korean-to-Chinese Machine Translation -- 2.2 Multilingual Unsupervised and Supervised Embeddings -- 3 Method
001482837 5058_ $$a3.1 Independent Language Feature Extraction Model -- 3.2 Translation Model -- 4 Experiment -- 4.1 Datasets -- 4.2 Settings -- 4.3 Main Results -- 5 Analysis -- 5.1 Ablation Experiments -- 5.2 Case Study -- 6 Conclusion -- References -- NJUNLP's Submission for CCMT 2023 Quality Estimation Task -- 1 Introduction -- 2 Methods -- 2.1 Unsupervised Methods -- 2.2 Supervised Methods -- 3 Experiments -- 3.1 Dataset -- 3.2 Settings -- 3.3 Single Model Results -- 3.4 Ensemble -- 4 Conclusion -- References -- HIT-MI&T Lab's Submission to CCMT 2023 Automatic Post-editing Task -- 1 Introduction
001482837 5058_ $$a2 Architecture -- 3 Data Augmentation -- 3.1 Synthetic Data Generation -- 3.2 ChatGPT-Based Data Augmentation -- 4 Experiments -- 4.1 Set-Up -- 4.2 Results of Different Architectures -- 4.3 Results of Data Augmentation -- 4.4 Results of Multi-model Ensemble -- 5 Conclusion -- References -- A k-Nearest Neighbor Approach for Domain-Specific Translation Quality Estimation -- 1 Introduction -- 2 Proposed Method -- 2.1 Overall Architecture -- 2.2 XLM-R Encoder -- 2.3 Classifier -- 2.4 k-Nearest Neighbor -- 2.5 Loss Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Settings -- 3.3 Results and Analysis
001482837 506__ $$aAccess limited to authorized users.
001482837 520__ $$aThis book constitutes the refereed proceedings of the 19th China Conference on Machine Translation, CCMT 2023, held in Jinan, China, during October 1921, 2023. The 8 full papers and 3 short papers included in this book were carefully reviewed and selected from 71 submissions. They focus on machine translation; improvement of translation models and systems; translation quality estimation; document-level machine translation; low-resource machine translation.
001482837 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 13, 2023).
001482837 650_0 $$aMachine translating$$vCongresses.$$vCongresses$$0(DLC)sh2010100238
001482837 650_0 $$aChinese language$$xMachine translating$$vCongresses.$$0(DLC)sh 85024251
001482837 655_0 $$aElectronic books.
001482837 7001_ $$aFeng, Yang,$$d1987-
001482837 7001_ $$aFeng, Chong.
001482837 77608 $$iPrint version:$$aFeng, Yang$$tMachine Translation$$dSingapore : Springer,c2023$$z9789819978939
001482837 830_0 $$aCommunications in computer and information science ;$$vv. 1922.
001482837 852__ $$bebk
001482837 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-7894-6$$zOnline Access$$91397441.1
001482837 909CO $$ooai:library.usi.edu:1482837$$pGLOBAL_SET
001482837 980__ $$aBIB
001482837 980__ $$aEBOOK
001482837 982__ $$aEbook
001482837 983__ $$aOnline
001482837 994__ $$a92$$bISE