Audience-specific Explanations for Machine Translation
Renhan Lou Jan Niehues
In machine translation, a common problem is that the translation of certain
words even if translated can cause incomprehension of the target language
audience due to different cultural backgrounds. A solution to solve this
problem is to add explanations for these words. In a first step, we therefore
need to identify these words or phrases. In this work we explore techniques to
extract example explanations from a parallel corpus. However, the sparsity of
sentences containing words that need to be explained makes building the
training dataset extremely difficult. In this work, we propose a semi-automatic
technique to extract these explanations from a large parallel corpus.
Experiments on English->German language pair show that our method is able to
extract sentence so that more than 10% of the sentences contain explanation,
while only 1.9% of the original sentences contain explanations. In addition,
experiments on English->French and English->Chinese language pairs also show
similar conclusions. This is therefore an essential first automatic step to
create a explanation dataset. Furthermore we show that the technique is robust
for all three language pairs.