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Deep Neural Networks (DNN) have been widely employed in industry to addressvarious Natural Language Processing (NLP) tasks. However, many engineers findit a big overhead when they have to choose from multiple frameworks, comparedifferent types of models, and understand various optimization mechanisms. AnNLP toolkit for DNN models with both generality and flexibility can greatlyimprove the productivity of engineers by saving their learning cost and guidingthem to find optimal solutions to their tasks. In this paper, we introduceNeuronBlocks\footnote{Code: \url{https://github.com/Microsoft/NeuronBlocks}}\footnote{Demo: \url{https://youtu.be/x6cOpVSZcdo}}, a toolkit encapsulating asuite of neural network modules as building blocks to construct various DNNmodels 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-2003demonstrate the effectiveness of NeuronBlocks.

NeuronBlocks: Building Your NLP DNN Models Like Playing Lego