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.

在专业领域内使用多语种机器翻译模型进行句子翻译时，通过广泛的数据集进行训练往往难以准确翻译。为了得到高质量的翻译，获取和翻译专门领域的数据成本很高，因此，通过无监督的方式找到最有效的数据，以降低标注成本变得实际可行。最近的研究表明，通过基于数据量来选择 “适度困难的数据” 可以找到这些有效数据，即数据既不过于困难也不过于简单，特别是在数据量有限的情况下。然而，我们发现在选择无监督数据时建立标准仍然具有挑战性，因为 “适度困难” 可能基于训练的数据领域而有所不同。本研究提出了一种新颖的无监督数据选择方法，即 “获取令人困惑的命名实体”，该方法采用翻译后命名实体的最大推理熵作为选择度量。其动机在于，专门领域数据中的命名实体被认为是数据最复杂的部分，应该以高置信度进行预测。通过对 “专门领域韩英平行语料库” 的验证，我们的方法在无监督数据选择方面表现出稳健性，与现有方法相比具有指导意义。