  Machine Translation (MT) has developed rapidly since the release of Large
Language Models and current MT evaluation is performed through comparison with
reference human translations or by predicting quality scores from human-labeled
data. However, these mainstream evaluation methods mainly focus on fluency and
factual reliability, whilst paying little attention to figurative quality. In
this paper, we investigate the figurative quality of MT and propose a set of
human evaluation metrics focused on the translation of figurative language. We
additionally present a multilingual parallel metaphor corpus generated by
post-editing. Our evaluation protocol is designed to estimate four aspects of
MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. In doing so,
we observe that translations of figurative expressions display different traits
from literal ones.
