NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (1904.09535v3)

Abstract: Deep Neural Networks (DNN) have been widely employed in industry to address various 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.

• The paper introduces NeuronBlocks, a toolkit that uses modular building blocks to streamline the construction of complex NLP DNN models.
• The paper demonstrates competitive benchmark performance on tasks like sequence labeling, GLUE, and WikiQA with minimal engineering overhead.
• The paper outlines future integrations with AutoML and multi-task learning to further enhance agile development of NLP models.

NeuronBlocks: Modular Construction of NLP Deep Neural Networks

The paper presents NeuronBlocks, a tool designed to streamline the development of Deep Neural Network (DNN) models specifically for NLP tasks. The authors address the significant overhead that engineers face when navigating multiple frameworks, model types, and optimization techniques. NeuronBlocks offers an efficient solution, enabling the construction of complex DNN models through modular building blocks, much like assembling with Lego pieces.

Challenges in NLP DNN Development

Engineers commonly encounter three major challenges when implementing NLP solutions using DNNs:

1. Multiple Frameworks: Familiarization with frameworks like TensorFlow and PyTorch is time-consuming.
2. Model Diversity and Evolution: Rapid advancements in architectures such as CNN, RNN, and Transform-based models require significant effort to comprehend.
3. Regularization and Optimization: Mastery of techniques such as dropout and mixed precision training is essential for enhancing model performance.

NeuronBlocks simplifies these complexities by providing a toolkit that abstracts these layers, reducing the learning curve and enabling engineers to focus on task-specific solutions efficiently.

Design of NeuronBlocks

NeuronBlocks is structured around two primary components: Block Zoo and Model Zoo.

• Block Zoo: This consists of standardized and reusable neural network components—such as embeddings, various RNNs, CNNs, and Transformers—encapsulated in a consistent interface, allowing interchangeable use and easy addition of custom modules.
• Model Zoo: Provides JSON-based configuration files for popular NLP tasks, which can serve as starting templates. This feature facilitates rapid deployment of DNN models with minimal coding.

NeuronBlocks supports multiple deployment environments, ensuring platform compatibility across CPU/GPU systems and various operating systems.

Evaluation and Results

NeuronBlocks demonstrates competitive performance across several benchmarks:

• Sequence Labeling: Experiments on the CoNLL-2003 dataset using various architectures (e.g., CRF, BiLSTM, CNN) reproduce or slightly improve upon literature-reported results.
• GLUE Benchmark: Models built with NeuronBlocks exhibit results on par with existing methods on several GLUE tasks, requiring minimal effort in model setup.
• Knowledge Distillation: NeuronBlocks efficiently utilizes a teacher-student paradigm to expedite inference, achieving notable speed gains with minor performance trade-offs.
• WikiQA Corpus: The toolkit delivers competitive outcomes using diverse models, emphasizing its flexibility in adapting to different task requirements.

Implications and Future Work

NeuronBlocks highlights the balance between flexibility and ease of use, catering to engineers across varying skill levels. The ability to quickly swap and test various architectures supports agile development cycles. Future extensions could explore integrations with automated machine learning (AutoML), extending support to multi-task learning and pre-trained model finetuning (e.g., BERT, GPT).

The paper establishes NeuronBlocks as a potential standard in modular DNN design for NLP applications, anticipating contributions and community-driven enhancement to broaden its scope and effectiveness.