Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editing and Their Comparative Study
K. Yogi, and C. Jha. Advanced Computational Intelligence: An International Journal (ACII), 2 (4):
19 - 26(October 2015)
DOI: 10.5121/acii.2015.2403
Abstract
Machine translation industry is working well but they have been facing problem in postediting. MT-outputs do not correct and fluent so minor or major changes need for publishing them. Postediting performs manually by linguists, which is expensive and time consuming. So we should select good translation for postediting among all translations. Various MT-evaluation metrics can be used for filter the good translations for postediting. We have shown the use of various MT-evolution metrics for selection of good translation and their comparative study
%0 Journal Article
%1 kuldeepyogiclassification
%A Yogi, Kuldeep
%A Jha, Chandra Kumar
%D 2015
%J Advanced Computational Intelligence: An International Journal (ACII)
%K BLEU FMEASURE Good METEOR MT-Engine MT-Evaluation Machine Postediting Translation translation
%N 4
%P 19 - 26
%R 10.5121/acii.2015.2403
%T Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editing and Their Comparative Study
%U http://airccse.org/journal/acii/vol2.html
%V 2
%X Machine translation industry is working well but they have been facing problem in postediting. MT-outputs do not correct and fluent so minor or major changes need for publishing them. Postediting performs manually by linguists, which is expensive and time consuming. So we should select good translation for postediting among all translations. Various MT-evaluation metrics can be used for filter the good translations for postediting. We have shown the use of various MT-evolution metrics for selection of good translation and their comparative study
@article{kuldeepyogiclassification,
abstract = {Machine translation industry is working well but they have been facing problem in postediting. MT-outputs do not correct and fluent so minor or major changes need for publishing them. Postediting performs manually by linguists, which is expensive and time consuming. So we should select good translation for postediting among all translations. Various MT-evaluation metrics can be used for filter the good translations for postediting. We have shown the use of various MT-evolution metrics for selection of good translation and their comparative study},
added-at = {2020-06-05T14:50:56.000+0200},
author = {Yogi, Kuldeep and Jha, Chandra Kumar},
biburl = {https://www.bibsonomy.org/bibtex/2a85b693152a81abbfc81d3c3d05265ac/janakirob},
doi = {10.5121/acii.2015.2403},
interhash = {a26b6c03e2590234a2223d51a772d283},
intrahash = {a85b693152a81abbfc81d3c3d05265ac},
journal = {Advanced Computational Intelligence: An International Journal (ACII)},
keywords = {BLEU FMEASURE Good METEOR MT-Engine MT-Evaluation Machine Postediting Translation translation},
month = {October},
number = 4,
pages = {19 - 26},
timestamp = {2023-08-02T14:01:48.000+0200},
title = {Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editing and Their Comparative Study},
url = {http://airccse.org/journal/acii/vol2.html},
volume = 2,
year = 2015
}