@inproceedings{durrett-etal-2021-contemporary,
title = "Contemporary {NLP} Modeling in Six Comprehensive Programming Assignments",
author = "Durrett, Greg and
Chen, Jifan and
Desai, Shrey and
Goyal, Tanya and
Kabela, Lucas and
Onoe, Yasumasa and
Xu, Jiacheng",
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-1.17",
doi = "10.18653/v1/2021.teachingnlp-1.17",
pages = "99--103",
abstract = "We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems. These assignments build from the ground up and emphasize full-stack understanding of machine learning models: initially, students implement inference and gradient computation by hand, then use PyTorch to build nearly state-of-the-art neural networks using current best practices. Topics are chosen to cover a wide range of modeling and inference techniques that one might encounter, ranging from linear models suitable for industry applications to state-of-the-art deep learning models used in NLP research. The assignments are customizable, with constrained options to guide less experienced students or open-ended options giving advanced students freedom to explore. All of them can be deployed in a fully autogradable fashion, and have collectively been tested on over 300 students across several semesters.",
}
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%0 Conference Proceedings
%T Contemporary NLP Modeling in Six Comprehensive Programming Assignments
%A Durrett, Greg
%A Chen, Jifan
%A Desai, Shrey
%A Goyal, Tanya
%A Kabela, Lucas
%A Onoe, Yasumasa
%A Xu, Jiacheng
%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 durrett-etal-2021-contemporary
%X We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems. These assignments build from the ground up and emphasize full-stack understanding of machine learning models: initially, students implement inference and gradient computation by hand, then use PyTorch to build nearly state-of-the-art neural networks using current best practices. Topics are chosen to cover a wide range of modeling and inference techniques that one might encounter, ranging from linear models suitable for industry applications to state-of-the-art deep learning models used in NLP research. The assignments are customizable, with constrained options to guide less experienced students or open-ended options giving advanced students freedom to explore. All of them can be deployed in a fully autogradable fashion, and have collectively been tested on over 300 students across several semesters.
%R 10.18653/v1/2021.teachingnlp-1.17
%U https://aclanthology.org/2021.teachingnlp-1.17
%U https://doi.org/10.18653/v1/2021.teachingnlp-1.17
%P 99-103
Markdown (Informal)
[Contemporary NLP Modeling in Six Comprehensive Programming Assignments](https://aclanthology.org/2021.teachingnlp-1.17) (Durrett et al., TeachingNLP 2021)
ACL