Publikation
Can Out-of-the-box NMT Beat a Domain-trained Moses on Technical Data?
Anne Beyer; Vivien Macketanz; Aljoscha Burchardt; Philip Williams
In: The 20th Annual Conference of the European Association for Machine Translation. Annual Conference of the European Association for Machine Translation (EAMT), 20th, May 29-31, Prague, Czech Republic, Pages 41-46, Charles University, 2017.
Zusammenfassung
In the last year, we have seen a lot of
evidence about the superiority of neu-
ral machine translation approaches (NMT)
over phrase-based statistical approaches
(PBMT). This trend has shown for the gen-
eral domain at public competitions such as
the WMT challenges as well as in the ob-
vious quality increase in online translation
services that have changed their technol-
ogy. In this paper, we take the perspective
of an LSP. The questions we want to an-
swer with this study is if now is already
the time to invest in the new technology.
To answer this question, we have collected
evidence as to whether an existing state-
of-the-art NMT system for the general do-
main can already compete with a domain-
trained and optimised Moses (PBMT) sys-
tem or if it is maybe already better. As it
is well known that automatic quality mea-
sures are not reliable for comparing the
performance of different system types, we
have performed a detailed manual evalua-
tion based on a test suite of domain seg-
ments.