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.

AllenNLP 是一个基于 PyTorch 平台的自然语言理解深度学习相关的可扩展实验框架，具有智能批量处理和填充的灵活数据 API，高层次的文本操作抽象和模块化可扩展的实验框架。