Bridging Background Knowledge Gaps in Translation with Automatic
  Explicitation
HyoJung Han Jordan Lee Boyd-Graber Marine Carpuat
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.