  Ethics regarding social bias has recently thrown striking issues in natural
language processing. Especially for gender-related topics, the need for a
system that reduces the model bias has grown in areas such as image captioning,
content recommendation, and automated employment. However, detection and
evaluation of gender bias in the machine translation systems are not yet
thoroughly investigated, for the task being cross-lingual and challenging to
define. In this paper, we propose a scheme for making up a test set that
evaluates the gender bias in a machine translation system, with Korean, a
language with gender-neutral pronouns. Three word/phrase sets are primarily
constructed, each incorporating positive/negative expressions or occupations;
all the terms are gender-independent or at least not biased to one side
severely. Then, additional sentence lists are constructed concerning formality
of the pronouns and politeness of the sentences. With the generated sentence
set of size 4,236 in total, we evaluate gender bias in conventional machine
translation systems utilizing the proposed measure, which is termed here as
translation gender bias index (TGBI). The corpus and the code for evaluation is
available on-line.
