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：翻译有助于人们理解以其他语言书写的内容。然而，当受众缺乏理解所需背景知识时，即使是正确的直译也无法实现这一目标。专业译员会通过考虑源语言与目标语言受众之间的文化差异，采用明示化策略来补充缺失的语境信息。尽管明示化具有帮助用户的潜力，但由于缺乏充分的评估方法，自然语言处理领域对其研究十分有限。本研究提出了自动生成明示化的技术，其动机源于我们基于维基百科构建并由人工译员标注的WikiExpl数据集。实验证明，所生成的明示化在多语言问答框架中能有效提升答案准确性。