NeuronBlocks: Building Your NLP DNN Models Like Playing Lego
Abstract
Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks. However, many engineers find it a big overhead when they have to choose from multiple frameworks, compare different types of models, and understand various optimization mechanisms. An NLP toolkit for DNN models with both generality and flexibility can greatly improve the productivity of engineers by saving their learning cost and guiding them to find optimal solutions to their tasks. In this paper, we introduce NeuronBlocks\footnote{Code: \url{https://github.com/Microsoft/NeuronBlocks}} \footnote{Demo: \url{https://youtu.be/x6cOpVSZcdo}}, a toolkit encapsulating a suite of neural network modules as building blocks to construct various DNN models with complex architecture. This toolkit empowers engineers to build, train, and test various NLP models through simple configuration of JSON files. The experiments on several NLP datasets such as GLUE, WikiQA and CoNLL-2003 demonstrate the effectiveness of NeuronBlocks.
Cite
@article{arxiv.1904.09535,
title = {NeuronBlocks: Building Your NLP DNN Models Like Playing Lego},
author = {Ming Gong and Linjun Shou and Wutao Lin and Zhijie Sang and Quanjia Yan and Ze Yang and Feixiang Cheng and Daxin Jiang},
journal= {arXiv preprint arXiv:1904.09535},
year = {2019}
}
Comments
6 pages, 3 figures