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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
In interactive machine translation (MT),
human translators correct errors in auto-
matic translations in collaboration with the
MT systems, which is seen as an effective
way to improve the productivity gain in
translation. In this study, we model source-
language syntactic constituency parse and
target-language syntactic descriptions in
the form of supertags as conditional con-
text for interactive prediction in neural
MT (NMT). We found that the supertags
significantly improve productivity gain in
translation in interactive-predictive NMT
(INMT), while syntactic parsing somewhat
found to be effective in reducing human
efforts in translation. Furthermore, when
we model this source- and target-language
syntactic information together as the con-
ditional context, both types complement
each other and our fully syntax-informed
INMT model shows statistically significant
reduction in human efforts for a French–
to–English translation task in a reference-
simulated setting, achieving 4.30 points
absolute (corresponding to 9.18% relative)
improvement in terms of word prediction
accuracy (WPA) and 4.84 points absolute
(corresponding to 9.01% relative) reduc-
tion in terms of word stroke ratio (WSR)
over the baseline
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