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    <titleInfo>
        <title>Writing Code for NLP Research</title>
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    <name type="personal">
        <namePart type="given">Matt</namePart>
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        <namePart type="given">Mark</namePart>
        <namePart type="family">Neumann</namePart>
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    <name type="personal">
        <namePart type="given">Joel</namePart>
        <namePart type="family">Grus</namePart>
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    <name type="personal">
        <namePart type="given">Nicholas</namePart>
        <namePart type="family">Lourie</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts</title>
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            <namePart>Mausam</namePart>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Doing modern NLP research requires writing code. Good code enables fast prototyping, easy debugging, controlled experiments, and accessible visualizations that help researchers understand what a model is doing. Bad code leads to research that is at best hard to reproduce and extend, and at worst simply incorrect. Indeed, there is a growing recognition of the importance of having good tools to assist good research in our field, as the upcoming workshop on open source software for NLP demonstrates. This tutorial aims to share best practices for writing code for NLP research, drawing on the instructors’ experience designing the recently-released AllenNLP toolkit, a PyTorch-based library for deep learning NLP research. We will explain how a library with the right abstractions and components enables better code and better science, using models implemented in AllenNLP as examples. Participants will learn how to write research code in a way that facilitates good science and easy experimentation, regardless of what framework they use.</abstract>
    <identifier type="citekey">gardner-etal-2018-writing</identifier>
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        <url>https://aclanthology.org/D18-3003</url>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Writing Code for NLP Research
%A Gardner, Matt
%A Neumann, Mark
%A Grus, Joel
%A Lourie, Nicholas
%Y Wang, Lu
%E Mausam
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Melbourne, Australia
%F gardner-etal-2018-writing
%X Doing modern NLP research requires writing code. Good code enables fast prototyping, easy debugging, controlled experiments, and accessible visualizations that help researchers understand what a model is doing. Bad code leads to research that is at best hard to reproduce and extend, and at worst simply incorrect. Indeed, there is a growing recognition of the importance of having good tools to assist good research in our field, as the upcoming workshop on open source software for NLP demonstrates. This tutorial aims to share best practices for writing code for NLP research, drawing on the instructors’ experience designing the recently-released AllenNLP toolkit, a PyTorch-based library for deep learning NLP research. We will explain how a library with the right abstractions and components enables better code and better science, using models implemented in AllenNLP as examples. Participants will learn how to write research code in a way that facilitates good science and easy experimentation, regardless of what framework they use.
%U https://aclanthology.org/D18-3003

Markdown (Informal)

[Writing Code for NLP Research](https://aclanthology.org/D18-3003) (Gardner et al., EMNLP 2018)

• Writing Code for NLP Research (Gardner et al., EMNLP 2018)

ACL

• Matt Gardner, Mark Neumann, Joel Grus, and Nicholas Lourie. 2018. Writing Code for NLP Research. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Melbourne, Australia. Association for Computational Linguistics.