Description

Book Synopsis
This book constitutes the refereed proceedings of the 17th China Conference on Machine Translation, CCMT 2020, held in Xining, China, in October 2021.
The 10 papers presented in this volume were carefully reviewed and selected from 25 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

Table of Contents
A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory.- SAUNLP'S Submission for CCMT 2021 Quality Estimation Task.- BJTU-Toshiba's Submission to CCMT 2021 QE and APE task.- Low-resource Neural Machine Translation based on Improved Reptile Meta-Learning Method.- Semantic Perception-Oriented Low-resource Neural Machine Translation.- Semantic-aware Deep Neural Attention Network for Machine Translation Detection.- Routing Based Context Selection for Document-Level Neural Machine Translation.- Generating Diverse Back-translations via Constraint Random Decoding.- Machine Translation Evaluation Technical Report for CCMT' 2021.- BJTU's Submission to CCMT 2021 Translation Evaluation Task.

Machine Translation: 17th China Conference, CCMT 2021, Xining, China, October 8–10, 2021, Revised Selected Papers

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    A Paperback by Jinsong Su, Rico Sennrich

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      View other formats and editions of Machine Translation: 17th China Conference, CCMT 2021, Xining, China, October 8–10, 2021, Revised Selected Papers by Jinsong Su

      Publisher: Springer Verlag, Singapore
      Publication Date: 30/10/2021
      ISBN13: 9789811675119, 978-9811675119
      ISBN10: 9811675112

      Description

      Book Synopsis
      This book constitutes the refereed proceedings of the 17th China Conference on Machine Translation, CCMT 2020, held in Xining, China, in October 2021.
      The 10 papers presented in this volume were carefully reviewed and selected from 25 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

      Table of Contents
      A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory.- SAUNLP'S Submission for CCMT 2021 Quality Estimation Task.- BJTU-Toshiba's Submission to CCMT 2021 QE and APE task.- Low-resource Neural Machine Translation based on Improved Reptile Meta-Learning Method.- Semantic Perception-Oriented Low-resource Neural Machine Translation.- Semantic-aware Deep Neural Attention Network for Machine Translation Detection.- Routing Based Context Selection for Document-Level Neural Machine Translation.- Generating Diverse Back-translations via Constraint Random Decoding.- Machine Translation Evaluation Technical Report for CCMT' 2021.- BJTU's Submission to CCMT 2021 Translation Evaluation Task.

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