Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not …
This paper presents a dataset for evaluating the machine translation of emotion-loaded user generated content. It contains human-annotated quality evaluation data and post-edited reference translations. The dataset is available at our GitHub …