Abstract
This paper describes the primarily-graduate computational linguistics and NLP curriculum at Georgetown University, a U.S. university that has seen significant growth in these areas in recent years. We reflect on the principles behind our curriculum choices, including recognizing the various academic backgrounds and goals of our students; teaching a variety of skills with an emphasis on working directly with data; encouraging collaboration and interdisciplinary work; and including languages beyond English. We reflect on challenges we have encountered, such as the difficulty of teaching programming skills alongside NLP fundamentals, and discuss areas for future growth.- Anthology ID:
- 2021.teachingnlp-1.11
- Volume:
- Proceedings of the Fifth Workshop on Teaching NLP
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- David Jurgens, Varada Kolhatkar, Lucy Li, Margot Mieskes, Ted Pedersen
- Venue:
- TeachingNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–69
- Language:
- URL:
- https://aclanthology.org/2021.teachingnlp-1.11
- DOI:
- 10.18653/v1/2021.teachingnlp-1.11
- Cite (ACL):
- Emma Manning, Nathan Schneider, and Amir Zeldes. 2021. A Balanced and Broadly Targeted Computational Linguistics Curriculum. In Proceedings of the Fifth Workshop on Teaching NLP, pages 65–69, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Balanced and Broadly Targeted Computational Linguistics Curriculum (Manning et al., TeachingNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.teachingnlp-1.11.pdf