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.

机器翻译中的一个常见问题是某些单词的翻译可能由于不同的文化背景导致目标语言受众难以理解。本研究探索从平行语料库中提取示例解释的技术，通过半自动的方法能够从大规模平行语料库中提取这些解释，为创建解释数据集提供了重要的自动化步骤。同时，通过实验证明该技术在三种语言对中均表现出鲁棒性。