Adapting Entities across Languages and Cultures

Denis Peskov, Viktor Hangya, Jordan Boyd-Graber, Alexander Fraser

Findings of EMNLP 2021, pages 3725-3750

How would you explain Bill Gates to a German? He is associated with
founding a company in the United States, so perhaps the German founder
Carl Benz could stand in for Gates in those contexts. This type of
translation is called adaptation in the translation community. Until
now, this task has not been done computationally. Automatic adaptation
could be used in natural language processing for machine translation
and indirectly for generating new question answering datasets and
education. We propose two automatic methods and compare them to human
results for this novel NLP task. First, a structured knowledge base
adapts named entities using their shared properties. Second,
vector-arithmetic and orthogonal embedding mappings methods identify
better candidates, but at the expense of interpretable features. We
evaluate our methods through a new dataset of human adaptations.