@inbook{618144f070b44f3fb17d03febd5cf163,
title = "Quantitative Analysis of Post-Editing Effort Indicators for NMT",
abstract = "The recent improvements in machine translation (MT) have boosted the use of post-editing (PE) in the translation industry. A new MT paradigm, neural MT (NMT), is displacing its corpus-based predecessor, statistical machine translation (SMT), in the translation workflows currently implemented because it usually increases the fluency and accuracy of the MT output. However, usual automatic measurements do not always indicate the quality of the MT output and there is still no clear correlation between PE effort and productivity. We present a quantitative analysis of different PE effort indicators for two NMT systems (transformer and seq2seq) for English-Spanish in-domain medical documents. We compare both systems and study the correlation between PE time and other scores.",
author = "Sergi Alvarez and Antoni Oliver and Toni Badia",
note = "Publisher Copyright: {\textcopyright}2020 The authors.",
year = "2020",
language = "English",
series = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, EAMT 2020",
publisher = "European Association for Machine Translation",
pages = "411--420",
editor = "Andre Martins and Helena Moniz and Sara Fumega and Bruno Martins and Fernando Batista and Luisa Coheur and \{Parra Escartiin\}, Carla and Isabel Trancoso and Marco Turchi and Arianna Bisazza and Joss Moorkens and Ana Guerberof and Mary Nurminen and Lena Marg and Forcada, \{Mikel L.\}",
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, EAMT 2020",
}