AllenNLP: A Deep Semantic Natural Language Processing Platform

Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer


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.
Anthology ID:
W18-2501
Volume:
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NLPOSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://www.aclweb.org/anthology/W18-2501
DOI:
10.18653/v1/W18-2501
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-2501.pdf