AllenNLP: A Deep Semantic Natural Language Processing Platform

TL;DR

AllenNLP is an open-source platform built on PyTorch that simplifies the development and experimentation of deep learning models for natural language understanding, supporting rapid research and innovation.

Contribution

It introduces a flexible, modular framework with high-level abstractions and reference implementations for core semantic tasks, facilitating easier research in NLP.

Findings

Supports quick model development and testing.

Provides high-quality reference implementations.

Enhances research productivity in NLP.

Abstract

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.