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.

本文关注机器翻译对情感文本的翻译表现，并通过提出的基于多维质量度量的评估框架，对谷歌翻译的输出进行评估。作者发现大约 50% 的机器翻译输出没有保留原始情感，并认为情感承载词和语言现象是这些翻译错误的常见原因。

评估汉英情感微博文本机器翻译：一份用于情感翻译质量评估的人工标注数据集

Evaluation of Chinese-English Machine Translation of Emotion-Loaded  Microblog Texts: A Human Annotated Dataset for the Quality Assessment of  Emotion Translation