Machine Translation
13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers
Published on 13. November 2017
XI, 125 pages
978-981-10-7134-8 (ISBN)
Description
This book constitutes the refereed proceedings of the 13th China Workshop on Machine Translation, CWMT 2017, held in Dalian, China, in September 2017.
The 10 papers presented in this volume were carefully reviewed and selected from 26 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
The 10 papers presented in this volume were carefully reviewed and selected from 26 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Singapore
Singapore
Publishing group
Springer Singapore
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 125 p. 25 illus.
File size
9,14 MB
ISBN-13
978-981-10-7134-8 (9789811071348)
DOI
10.1007/978-981-10-7134-8
Schweitzer Classification
Other editions
Additional editions
Derek F. Wong | Deyi Xiong
Machine Translation
13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers
Book
11/2017
Springer
€53.49
Shipment within 15-20 days
Content
Neural Machine Translation with Phrasal Attention.- Singleton Detection for Coreference Resolution via Multi-window and Multi-Filter CNN.- A Method of Unknown Words Processing for Neural Machine Translation Using HowNet.- Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT.- An Unknown Word Processing Method in NMT by Integrating Syntactic Structure and Semantic Concept.- RGraph: Generating Reference Graphs for Better Machine Translation Evaluation.- ENTF: An Entropy-based MT Evaluation Metric.- Translation Oriented Sentence Level Collocation Identification and Extraction.- Combining Domain Knowledge and Deep Learning Makes NMT More Adaptive.- Handling Many-To-One UNK Translation for Neural Machine Translation.- A Content-based Neural Reordering Model for Statistical Machine Translation.
