@inproceedings{gardner-etal-2018-allennlp,
title = "{A}llen{NLP}: A Deep Semantic Natural Language Processing Platform",
author = "Gardner, Matt and
Grus, Joel and
Neumann, Mark and
Tafjord, Oyvind and
Dasigi, Pradeep and
Liu, Nelson F. and
Peters, Matthew and
Schmitz, Michael and
Zettlemoyer, Luke",
editor = "Park, Eunjeong L. and
Hagiwara, Masato and
Milajevs, Dmitrijs and
Tan, Liling",
booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2501",
doi = "10.18653/v1/W18-2501",
pages = "1--6",
abstract = "Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.",
}
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<abstract>Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.</abstract>
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%0 Conference Proceedings
%T AllenNLP: A Deep Semantic Natural Language Processing Platform
%A Gardner, Matt
%A Grus, Joel
%A Neumann, Mark
%A Tafjord, Oyvind
%A Dasigi, Pradeep
%A Liu, Nelson F.
%A Peters, Matthew
%A Schmitz, Michael
%A Zettlemoyer, Luke
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Tan, Liling
%S Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F gardner-etal-2018-allennlp
%X Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.
%R 10.18653/v1/W18-2501
%U https://aclanthology.org/W18-2501
%U https://doi.org/10.18653/v1/W18-2501
%P 1-6
Markdown (Informal)
[AllenNLP: A Deep Semantic Natural Language Processing Platform](https://aclanthology.org/W18-2501) (Gardner et al., NLPOSS 2018)
ACL
- Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018. AllenNLP: A Deep Semantic Natural Language Processing Platform. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS), pages 1–6, Melbourne, Australia. Association for Computational Linguistics.