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Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study., , , , and . CoRR, (2021)Sleeping with the enemy ? Or should translators work with Google Translate ?, and . Conducting research in translation technologies, chapter 2, Peter Lang, Bern, (2015)When less is more in Neural Quality Estimation of Machine Translation. An industry case study., , , , , , and . MTSummit (2), page 228-235. European Association for Machine Translation, (2019)Analysing Mistranslation of Emotions in Multilingual Tweets by Online MT Tools., , , , and . EAMT, page 275-284. European Association for Machine Translation, (2023)Does Machine Translation Really Produce Translations?. EAMT, (2018)APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task., , and . WMT (3), page 132-138. Association for Computational Linguistics, (2019)Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete Feature.. EAMT, page 421-430. European Association for Machine Translation, (2020)A review of the state-of-the-art in automatic post-editing., , , , , , , , and . Mach. Transl., 35 (2): 101-143 (2021)A roadmap to neural automatic post-editing: an empirical approach., , , , , , , , and . Mach. Transl., 34 (2-3): 67-96 (2020)Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation., , , , and . EAMT, page 125-135. European Association for Machine Translation, (2023)