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        <title>From back to the roots into the gated woods: Deep learning for NLP</title>
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            <namePart type="given">David</namePart>
            <namePart type="family">Jurgens</namePart>
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        <name type="personal">
            <namePart type="given">Varada</namePart>
            <namePart type="family">Kolhatkar</namePart>
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        <name type="personal">
            <namePart type="given">Lucy</namePart>
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        <name type="personal">
            <namePart type="given">Margot</namePart>
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    <abstract>Deep neural networks have revolutionized many fields, including Natural Language Processing. This paper outlines teaching materials for an introductory lecture on deep learning in Natural Language Processing (NLP). The main submitted material covers a summer school lecture on encoder-decoder models. Complementary to this is a set of jupyter notebook slides from earlier teaching, on which parts of the lecture were based on. The main goal of this teaching material is to provide an overview of neural network approaches to natural language processing, while linking modern concepts back to the roots showing traditional essential counterparts. The lecture departs from count-based statistical methods and spans up to gated recurrent networks and attention, which is ubiquitous in today’s NLP.</abstract>
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    <identifier type="doi">10.18653/v1/2021.teachingnlp-1.9</identifier>
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%0 Conference Proceedings
%T From back to the roots into the gated woods: Deep learning for NLP
%A Plank, Barbara
%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 plank-2021-back
%X Deep neural networks have revolutionized many fields, including Natural Language Processing. This paper outlines teaching materials for an introductory lecture on deep learning in Natural Language Processing (NLP). The main submitted material covers a summer school lecture on encoder-decoder models. Complementary to this is a set of jupyter notebook slides from earlier teaching, on which parts of the lecture were based on. The main goal of this teaching material is to provide an overview of neural network approaches to natural language processing, while linking modern concepts back to the roots showing traditional essential counterparts. The lecture departs from count-based statistical methods and spans up to gated recurrent networks and attention, which is ubiquitous in today’s NLP.
%R 10.18653/v1/2021.teachingnlp-1.9
%U https://aclanthology.org/2021.teachingnlp-1.9
%U https://doi.org/10.18653/v1/2021.teachingnlp-1.9
%P 59-61

Markdown (Informal)

[From back to the roots into the gated woods: Deep learning for NLP](https://aclanthology.org/2021.teachingnlp-1.9) (Plank, TeachingNLP 2021)

• From back to the roots into the gated woods: Deep learning for NLP (Plank, TeachingNLP 2021)

ACL

• Barbara Plank. 2021. From back to the roots into the gated woods: Deep learning for NLP. In Proceedings of the Fifth Workshop on Teaching NLP, pages 59–61, Online. Association for Computational Linguistics.