Robust Guidance for Unsupervised Data Selection: Capturing Perplexing
  Named Entities for Domain-Specific Machine Translation
Seunghyun Ji Hagai Raja Sinulingga Darongsae Kwon
Employing extensive datasets enables the training of multilingual machine
translation models; however, these models often fail to accurately translate
sentences within specialized domains. Although obtaining and translating
domain-specific data incurs high costs, it is inevitable for high-quality
translations. Hence, finding the most 'effective' data with an unsupervised
setting becomes a practical strategy for reducing labeling costs. Recent
research indicates that this effective data could be found by selecting
'properly difficult data' based on its volume. This means the data should not
be excessively challenging or overly simplistic, especially if the amount of
data is limited. However, we found that establishing a criterion for
unsupervised data selection remains challenging, as the 'proper difficulty'
might vary based on the data domain being trained on. We introduce a novel
unsupervised data selection method, 'Capturing Perplexing Named Entities',
which adopts the maximum inference entropy in translated named entities as a
selection measure. The motivation was that named entities in domain-specific
data are considered the most complex portion of the data and should be
predicted with high confidence. When verified with the 'Korean-English Parallel
Corpus of Specialized Domains,' our method served as a robust guidance for
unsupervised data selection, in contrast to existing methods.