Improving Quality Estimation of Machine Translation by Using Pre-trained Language Representation

2019 ◽  
pp. 11-22
Author(s):  
Guoyi Miao ◽  
Hui Di ◽  
Jinan Xu ◽  
Zhongcheng Yang ◽  
Yufeng Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Quality Estimation ◽  
Language Representation

Dimensionality reduction methods for machine translation quality estimation

2013 ◽  
Vol 27 (3-4) ◽  
pp. 281-301 ◽  
Author(s):  
Jesús González-Rubio ◽  
J. Ramón Navarro-Cerdán ◽  
Francisco Casacuberta
Keyword(s):  
Dimensionality Reduction ◽  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Reduction Methods

scholarly journals Syntax and Semantics in Quality Estimation of Machine Translation

2014 ◽  
Author(s):  
Rasoul Kaljahi ◽  
Jennifer Foster ◽  
Johann Roturier
Keyword(s):  
Machine Translation ◽  
Quality Estimation

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

Quality estimation of machine translation for literature

2018 ◽  
pp. 183-208
Author(s):  
Mozhgan Ghassemiazghandi ◽  
Tengku Sepora Tengku Mahadi
Keyword(s):  
Machine Translation ◽  
Quality Estimation

A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory

2021 ◽  
pp. 1-15
Author(s):  
Yidong Chen ◽  
Enjun Zhong ◽  
Yiqi Tong ◽  
Yanru Qiu ◽  
Xiaodong Shi
Keyword(s):  
Machine Translation ◽  
Estimation Model ◽  
Quality Estimation ◽  
Translation Quality ◽  
Model Based ◽  
Centering Theory ◽  
Document Level

Quality estimation for machine translation: preface

2013 ◽  
Vol 27 (3-4) ◽  
pp. 167-170
Author(s):  
Lucia Specia ◽  
Radu Soricut
Keyword(s):  
Machine Translation ◽  
Quality Estimation

scholarly journals Self-Supervised Quality Estimation for Machine Translation

2021 ◽  
Author(s):  
Yuanhang Zheng ◽  
Zhixing Tan ◽  
Meng Zhang ◽  
Mieradilijiang Maimaiti ◽  
Huanbo Luan ◽  
...  
Keyword(s):  
Machine Translation ◽  
Quality Estimation

scholarly journals UAlacant word-level machine translation quality estimation system at WMT 2015

2015 ◽  
Author(s):  
Miquel Esplà-Gomis ◽  
Felipe Sánchez-Martínez ◽  
Mikel Forcada
Keyword(s):  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Word Level ◽  
Estimation System
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