Perceptions and experiences of machine translation (MT) users before, during, and after their interaction with MT systems, products or services has been overlooked both in academia and in industry. Tradi-tionally, the focus has been on productivi-ty and quality, often neglecting the human factor. We propose the concept of Ma-chine Translation User Experience (MTUX) for assessing, evaluating, and getting further information about the user experiences of people interacting with MT. By conducting a human-computer in-teraction (HCI)-based study with 15 pro-fessional translators, we analyse which is the best method for measuring MTUX, and conclude by suggesting the use of the User Experience Questionnaire (UEQ). The measurement of MTUX will help eve-ry stakeholder in the MT industry - devel-opers will be able to identify pain points for the users and solve them in the devel-opment process, resulting in better MTUX and higher adoption of MT systems or products by MT users.

Detection of occupations in texts is relevant for a range of important application scenarios, like competitive intelligence, sociodemographic analysis, legal NLP or health-related occupational data mining. Despite the importance and heterogeneous data types that mention occupations, text mining efforts to recognize them have been limited. This is due to the lack of clear annotation guidelines and high-quality Gold Standard corpora. Social media data can be regarded as a relevant source of information for real-time monitoring of at-risk occupational groups in the context of pandemics like the COVID-19 one, facilitating intervention strategies for occupations in direct contact with infectious agents or affected by mental health issues. To evaluate current NLP methods and to generate resources, we have organized the ProfNER track at SMM4H 2021, providing ProfNER participants with a Gold Standard corpus of manually annotated tweets (human IAA of 0.919) following annotation guidelines available in Spanish and English, an occupation gazetteer, a machine-translated version of tweets, and FastText embeddings. Out of 35 registered teams, 11 submitted a total of 27 runs. Best-performing participants built systems based on recent NLP technologies (e.g. transformers) and achieved 0.93 F-score in Text Classification and 0.839 in Named Entity Recognition. Corpus: https://doi.org/10.5281/zenodo.4309356