Machine Translation: 13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers

Machine Translation: 13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers

by Derek F. Wong, Deyi Xiong
ISBN-10:
9811071330
ISBN-13:
9789811071331
Pub. Date:
12/19/2017
Publisher:
Springer Nature Singapore
ISBN-10:
9811071330
ISBN-13:
9789811071331
Pub. Date:
12/19/2017
Publisher:
Springer Nature Singapore
Machine Translation: 13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers

Machine Translation: 13th China Workshop, CWMT 2017, Dalian, China, September 27-29, 2017, Revised Selected Papers

by Derek F. Wong, Deyi Xiong
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Overview

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.

Product Details

ISBN-13: 9789811071331
Publisher: Springer Nature Singapore
Publication date: 12/19/2017
Series: Communications in Computer and Information Science , #787
Edition description: 1st ed. 2017
Pages: 125
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

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.

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