From Mixed Backgrounds to NLP Skills

AbstractStudent demand for NLP training now spans linguistics, computer science, data science, and applied fields, producing cohorts with uneven preparation. We report on a four-course curriculum used in an M.S. Computational Linguistics program: an undergraduate on-ramp, a two-course graduate core (classical methods and neural/LLM methods), and a rotating special-topics seminar. We describe the role of each course, the bridging strategy that keeps the core sequence focused, and assessment patterns that emphasize error analysis, experimental reasoning, and reproducible practice. The goal is a set of reusable curricular design patterns for mixed-background programs facing rapid topic turnover in NLP.