Translations help people understand content written in another language.
However, even correct literal translations do not fulfill that goal when people
lack the necessary background to understand them. Professional translators
incorporate explicitations to explain the missing context by considering
cultural differences between source and target audiences. Despite its potential
to help users, NLP research on explicitation is limited because of the dearth
of adequate evaluation methods. This work introduces techniques for
automatically generating explicitations, motivated by WikiExpl: a dataset that
we collect from Wikipedia and annotate with human translators. The resulting
explicitations are useful as they help answer questions more accurately in a
multilingual question answering framework.

翻译帮助人们理解用另一种语言编写的内容，但即使是正确的直译，缺乏必要的背景也无法实现这一目标。为了弥补这一缺失的语境理解，专业翻译人员通过考虑源语言和目标受众之间的文化差异来进行明确说明。然而，由于缺乏适当的评估方法，NLP 研究对明确说明的探索还有限。本研究引入了一种基于从维基百科收集并由人类翻译人员注释的数据集「WikiExpl」的技术，以自动生成明确说明。所得到的明确说明在多语言问答框架中非常有用，能够更准确地回答问题。