This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) high-level abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.
@article{arxiv.1803.07640,
title = {AllenNLP: A Deep Semantic Natural Language Processing Platform},
author = {Matt Gardner and Joel Grus and Mark Neumann and Oyvind Tafjord and Pradeep Dasigi and Nelson Liu and Matthew Peters and Michael Schmitz and Luke Zettlemoyer},
journal= {arXiv preprint arXiv:1803.07640},
year = {2018}
}
Comments
Describes the initial version of AllenNLP. Many features and models have been added since the first release. This is the paper to cite if you use AllenNLP in your research. Updated 5/31/2018 with version accepted to the NLP OSS workshop help at ACL 2018