Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
In this paper, we focus on how current Machine Translation (MT) tools perform
on the translation of emotion-loaded texts by evaluating outputs from Google
Translate according to a framework proposed in this paper. We propose this
evaluation framework based on the Multidimensional Quality Metrics (MQM) and
perform a detailed error analysis of the MT outputs. From our analysis, we
observe that about 50% of the MT outputs fail to preserve the original emotion.
After further analysis of the errors, we find that emotion carrying words and
linguistic phenomena such as polysemous words, negation, abbreviation etc., are
common causes for these translation errors.