TY - JOUR
T1 - Post-editing neural machine translation versus translation memory segments
AU - Sánchez-Gijón, Pilar
AU - Moorkens, Joss
AU - Way, Andy
PY - 2019/6/15
Y1 - 2019/6/15
N2 - © 2019, Springer Nature B.V. The use of neural machine translation (NMT) in a professional scenario implies a number of challenges despite growing evidence that, in language combinations such as English to Spanish, NMT output quality has already outperformed statistical machine translation in terms of automatic metric scores. This article presents the result of an empirical test that aims to shed light on the differences between NMT post-editing and translation with the aid of a translation memory (TM). The results show that NMT post-editing involves less editing than TM segments, but this editing appears to take more time, with the consequence that NMT post-editing does not seem to improve productivity as may have been expected. This might be due to the fact that NMT segments show a higher variability in terms of quality and time invested in post-editing than TM segments that are ‘more similar’ on average. Finally, results show that translators who perceive that NMT boosts their productivity actually performed faster than those who perceive that NMT slows them down.
AB - © 2019, Springer Nature B.V. The use of neural machine translation (NMT) in a professional scenario implies a number of challenges despite growing evidence that, in language combinations such as English to Spanish, NMT output quality has already outperformed statistical machine translation in terms of automatic metric scores. This article presents the result of an empirical test that aims to shed light on the differences between NMT post-editing and translation with the aid of a translation memory (TM). The results show that NMT post-editing involves less editing than TM segments, but this editing appears to take more time, with the consequence that NMT post-editing does not seem to improve productivity as may have been expected. This might be due to the fact that NMT segments show a higher variability in terms of quality and time invested in post-editing than TM segments that are ‘more similar’ on average. Finally, results show that translators who perceive that NMT boosts their productivity actually performed faster than those who perceive that NMT slows them down.
KW - MT acceptance
KW - Neural machine translation
KW - Translation memory
KW - Translation productivity
KW - Translation quality perception
UR - https://ddd.uab.cat/record/203939
U2 - https://doi.org/10.1007/s10590-019-09232-x
DO - https://doi.org/10.1007/s10590-019-09232-x
M3 - Article
VL - 33
SP - 31
EP - 59
JO - Machine Translation
JF - Machine Translation
SN - 0922-6567
ER -