Terminology Translation Error Identification and Correction

2017 ◽  
pp. 141-152
Author(s):  
Mengyi Liu ◽  
Jian Tang ◽  
Yu Hong ◽  
Jianmin Yao
Keyword(s):  
Error Identification ◽  
Translation Error

Automatic machine translation error identification

2014 ◽  
Vol 29 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Débora Beatriz de Jesus Martins ◽  
Helena de Medeiros Caseli
Keyword(s):  
Machine Translation ◽  
Automatic Machine ◽  
Error Identification ◽  
Translation Error

Model validation for control and controller validation in a prediction error identification framework—Part I: theory

Automatica ◽  
2003 ◽  
Vol 39 (3) ◽  
pp. 403-415 ◽  
Author(s):  
Michel Gevers ◽  
Xavier Bombois ◽  
Benoı̂t Codrons ◽  
Gérard Scorletti ◽  
Brian D.O. Anderson
Keyword(s):  
Model Validation ◽  
Prediction Error ◽  
Error Identification

scholarly journals UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

2020 ◽  
Vol 10 (11) ◽  
pp. 3904
Author(s):  
Van-Hai Vu ◽  
Quang-Phuoc Nguyen ◽  
Joon-Choul Shin ◽  
Cheol-Young Ock
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Ambiguous Word ◽  
High Rate ◽  
Word Sense ◽  
Language Resources ◽  
Parallel Corpora ◽  
Knowledge Based ◽  
Translation Error ◽  
Translation Study

Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.

Data-driven model reference control design by prediction error identification

2017 ◽  
Vol 354 (6) ◽  
pp. 2628-2647 ◽  
Author(s):  
Lucíola Campestrini ◽  
Diego Eckhard ◽  
Alexandre Sanfelice Bazanella ◽  
Michel Gevers
Keyword(s):  
Prediction Error ◽  
Control Design ◽  
Data Driven ◽  
Error Identification ◽  
Model Reference ◽  
Model Reference Control

Modeling and error identification on three-dimensional tomosynthetic reconstructions

2000 ◽  
Author(s):  
Paul F. Hemler ◽  
Richard L. Webber ◽  
Fredrick H. Fahey
Keyword(s):  
Three Dimensional ◽  
Error Identification
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