AllenNLP: A Deep Semantic Natural Language Processing Platform
AllenNLP: A Deep Semantic Natural Language Processing Platform
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DOI：
10.18653/v1/w18-2501
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发表时间：
2018-03
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影响因子：
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通讯作者：
Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord;Pradeep Dasigi;Nelson F. Liu;Matthew E. Peters
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文献类型：
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作者：
Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord;Pradeep Dasigi;Nelson F. Liu;Matthew E. Peters
现代自然语言处理（NLP）研究需要编写代码。理想情况下，该代码将提供方法的精确定义，结果的容易重复性，以及扩展研究的基础。然而，许多研究代码库将高级参数隐藏在实现细节之下，运行和调试具有挑战性，并且很难扩展，因此更有可能被重写。本文介绍了AllenNLP，这是一个将深度学习方法应用于NLP研究的库，它通过易于使用的命令行工具、声明性配置驱动实验和模块化NLP抽象来解决这些问题。AllenNLP已经提高了艾伦人工智能研究所的研究实验率和NLP组件的共享率，我们正在努力在整个领域产生同样的影响。
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.