  Low-resourced data presents a significant challenge for neural machine
translation. In most cases, the low-resourced environment is caused by high
costs due to the need for domain experts or the lack of language experts.
Therefore, identifying the most training-efficient data within an unsupervised
setting emerges as a practical strategy. Recent research suggests that such
effective data can be identified by selecting 'appropriately complex data'
based on its volume, providing strong intuition for unsupervised data
selection. However, we have discovered that establishing criteria for
unsupervised data selection remains a challenge, as the 'appropriate level of
difficulty' may vary depending on the data domain. We introduce a novel
unsupervised data selection method named 'Capturing Perplexing Named Entities,'
which leverages the maximum inference entropy in translated named entities as a
metric for selection. When tested with the 'Korean-English Parallel Corpus of
Specialized Domains,' our method served as robust guidance for identifying
training-efficient data across different domains, in contrast to existing
methods.
