Interactive Assignments for Teaching Structured Neural NLP

David Gaddy, Daniel Fried, Nikita Kitaev, Mitchell Stern, Rodolfo Corona, John DeNero, Dan Klein


Abstract
We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical forms), modern neural architectures (LSTMs and Transformers), inference algorithms (dynamic programs and approximate search) and training methods (full and weak supervision). We designed assignments to build incrementally both within each assignment and across assignments, with the goal of enabling students to undertake graduate-level research in NLP by the end of the course.
Anthology ID:
2021.teachingnlp-1.18
Volume:
Proceedings of the Fifth Workshop on Teaching NLP
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | TeachingNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–107
Language:
URL:
https://aclanthology.org/2021.teachingnlp-1.18
DOI:
10.18653/v1/2021.teachingnlp-1.18
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.teachingnlp-1.18.pdf