GenAI, though not developed specifically for translation, has shown the potential to produce translations as good as, if not better than, contemporary neural machine translation systems. In the context of tertiary-level translator education, the integration of GenAI has renewed debate in curricula and pedagogy. Despite divergent opinions among educators, it is evident that translation students, like many other students, are using GenAI tools to facilitate translation tasks as they use MT tools. We thus argue for the benefits of guiding students in using GenAI in an informed, critical, and ethical manner. To provide insights for tailored curriculum and pedagogy, it is insightful to investigate what students use GenAI for and how they use it. This study is among the first to investigate translation students’ prompting behaviours. For thematic and discourse analysis, we collected prompts in GenAI tools generated by a representative sample of postgraduate student participants for eight months. The findings revealed that students had indeed used GenAI in various translation tasks, but their prompting behaviours were intuitive and uninformed. Our findings suggest an urgent need for translation educators to consider students’ agency and critical engagement with GenAI tools.

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first task is automatic classification of tweets that mention medications and the second task is automatic classification of tweets in English that report adverse effects. The system we propose for these tasks is based on bidirectional encoder representations from transformers (BERT) incorporating with knowledge graph and retrieving evidence from online information. Our system achieves an F1 of 0.7553 in task 1 and an F1 of 0.5455 in task 2.