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Machine Translation : 17th China Conference, CCMT 2021, Xining, China, October 8-10, 2021, Revised Selected Papers / edited by Jinsong Su, Rico Sennrich.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Communications in computer and information science 1865-0937 ; 1464
- Communications in Computer and Information Science, 1865-0937 ; 1464
- Language:
- English
- Subjects (All):
- Natural language processing (Computer science).
- Database management.
- Computer science.
- Coding theory.
- Information theory.
- Information storage and retrieval systems.
- Computer science-Mathematics.
- Mathematical statistics.
- Natural Language Processing (NLP).
- Database Management.
- Computer Science Logic and Foundations of Programming.
- Coding and Information Theory.
- Information Storage and Retrieval.
- Probability and Statistics in Computer Science.
- Local Subjects:
- Natural Language Processing (NLP).
- Database Management.
- Computer Science Logic and Foundations of Programming.
- Coding and Information Theory.
- Information Storage and Retrieval.
- Probability and Statistics in Computer Science.
- Physical Description:
- 1 online resource (XIII, 125 pages) : 52 illustrations, 40 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- 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.
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-7512-6
- 9789811675126
- Access Restriction:
- Restricted for use by site license.
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