@inproceedings{gaddy-etal-2021-interactive,
title = "Interactive Assignments for Teaching Structured Neural {NLP}",
author = "Gaddy, David and
Fried, Daniel and
Kitaev, Nikita and
Stern, Mitchell and
Corona, Rodolfo and
DeNero, John and
Klein, Dan",
editor = "Jurgens, David and
Kolhatkar, Varada and
Li, Lucy and
Mieskes, Margot and
Pedersen, Ted",
booktitle = "Proceedings of the Fifth Workshop on Teaching NLP",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.teachingnlp-1.18/",
doi = "10.18653/v1/2021.teachingnlp-1.18",
pages = "104--107",
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."
}
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%0 Conference Proceedings
%T Interactive Assignments for Teaching Structured Neural NLP
%A Gaddy, David
%A Fried, Daniel
%A Kitaev, Nikita
%A Stern, Mitchell
%A Corona, Rodolfo
%A DeNero, John
%A Klein, Dan
%Y Jurgens, David
%Y Kolhatkar, Varada
%Y Li, Lucy
%Y Mieskes, Margot
%Y Pedersen, Ted
%S Proceedings of the Fifth Workshop on Teaching NLP
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F gaddy-etal-2021-interactive
%X 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.
%R 10.18653/v1/2021.teachingnlp-1.18
%U https://aclanthology.org/2021.teachingnlp-1.18/
%U https://doi.org/10.18653/v1/2021.teachingnlp-1.18
%P 104-107
Markdown (Informal)
[Interactive Assignments for Teaching Structured Neural NLP](https://aclanthology.org/2021.teachingnlp-1.18/) (Gaddy et al., TeachingNLP 2021)
ACL