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 journals/bioinformatics/OrtellsCL93
%A Kuldeep, Yogi
%A Chandra Kumar, Jha
%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 8
%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/papers/2415acii03.pdf
%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{journals/bioinformatics/OrtellsCL93,
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 = {2018-01-02T06:23:48.000+0100},
author = {Kuldeep, Yogi and Chandra Kumar, Jha},
biburl = {https://www.bibsonomy.org/bibtex/2a6e90e6763b6f15060b1a5880e66a969/janakirob},
doi = {10.5121/acii.2015.2403},
ee = {http://dx.doi.org/10.1093/bioinformatics/9.6.741},
interhash = {fa29a14003ecdb8c7833cbd527ba1fd1},
intrahash = {a6e90e6763b6f15060b1a5880e66a969},
issn = {2454 - 3934},
journal = { Advanced Computational Intelligence: An International Journal (ACII)},
keywords = {BLEU FMEASURE Good METEOR MT-Engine MT-Evaluation Machine Postediting Translation translation},
language = {English},
month = {October},
number = 4,
pages = 8,
timestamp = {2018-01-02T06:23:48.000+0100},
title = {Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editing and Their Comparative Study},
url = {http://airccse.org/journal/acii/papers/2415acii03.pdf},
volume = 2,
year = 2015
}