Classification of Machine Translation Outputs Using NB Classifier and SVM for Post-Editing
نویسندگان
چکیده
منابع مشابه
Productivity and quality in the post-editing of outputs from translation memories and machine translation
Machine-translated segments are increasingly included as fuzzy matches within the translation-memory systems in the localisation workflow. This study presents preliminary results on the correlation between these two types of segments in terms of productivity and final quality. In order to test these variables, we set up an experiment with a group of eight professional translators using an on-li...
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In this paper we investigate ways in which information from the postediting of machine translations can be used to rank translation systems for quality. In addition to the commonly used edit distance between the raw translation and its edited version, we consider post-editing time and keystroke logging, since these can account not only for technical effort, but also cognitive effort. In this sy...
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Given the significant improvements in Machine Translation (MT) quality and the increasing demand for translations, post-editing of automatic translations is becoming a popular practice in the translation industry. It has been shown to allow for larger volumes of translations to be produced, saving time and costs. In addition, the post-editing of automatic translations can help understand proble...
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In the context of massive adoption of Machine Translation (MT) by human localization services in Post-Editing (PE) workflows, we analyze the activity of post-editing high quality translations through a novel PE analysis methodology. We define and introduce a new unit for evaluating post-editing effort based on Post-Editing Action (PEA) for which we provide human evaluation guidelines and propos...
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ژورنال
عنوان ژورنال: Machine Learning and Applications: An International Journal
سال: 2015
ISSN: 2394-0840
DOI: 10.5121/mlaij.2015.2403