Promoting Target Data in Context-aware Neural Machine Translation
Harritxu Gete Thierry Etchegoyhen
Standard context-aware neural machine translation (NMT) typically relies on
parallel document-level data, exploiting both source and target contexts.
Concatenation-based approaches in particular, still a strong baseline for
document-level NMT, prepend source and/or target context sentences to the
sentences to be translated, with model variants that exploit equal amounts of
source and target data on each side achieving state-of-the-art results. In this
work, we investigate whether target data should be further promoted within
standard concatenation-based approaches, as most document-level phenomena rely
on information that is present on the target language side. We evaluate novel
concatenation-based variants where the target context is prepended to the
source language, either in isolation or in combination with the source context.
Experimental results in English-Russian and Basque-Spanish show that including
target context in the source leads to large improvements on target language
phenomena. On source-dependent phenomena, using only target language context in
the source achieves parity with state-of-the-art concatenation approaches, or
slightly underperforms, whereas combining source and target context on the
source side leads to significant gains across the board.