Social media companies as well as censorship authorities make extensive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train c
omputers, automatic-translation of the online content is usually implemented to compensate for the scarcity of text in some languages. However, machine translation (MT) mistakes are a regular occurrence when translating sentiment-oriented user-generated content (UGC), especially when a low-resource language is involved. In such scenarios, the adequacy of the whole process relies on the assumption that the translation can be evaluated correctly. In this paper, we assess the ability of automatic quality metrics to detect critical machine translation errors which can cause serious misunderstanding of the affect message. We compare the performance of three canonical metrics on meaningless translations as compared to meaningful translations with a critical error that distorts the overall sentiment of the source text. We demonstrate the need for the fine-tuning of automatic metrics to make them more robust in detecting sentiment critical errors.
