Mobile NLP Model via Knowledge Distillation
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Solution Overview
Problem
High-capacity natural language understanding models are computationally and memory-intensive, making them unsuitable for deployment in mobile or constrained computing environments.
Innovation Solution
A teacher-student paradigm is employed to generate a lightweight, mobile-friendly NLP model. A teacher network is first trained using a large text corpus and then fine-tuned on domain-specific data. A student network is then trained to mimic the teacher network's outputs, resulting in a compact model suitable for mobile deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high capacity NLP models are used to automate data extraction, then extraction accuracy and performance are improved, but compute power and memory requirements increase significantly
Solution Approach 1:
The patent creates a student model that copies the essential functionality of the teacher model through knowledge distillation. The student model is trained to replicate the teacher model's output distribution while using a simpler architecture with fewer parameters, enabling deployment in mobile environments without sacrificing extraction accuracy
Solution Approach 2:
The patent changes the parameter scale from the teacher model's large parameter count to the student model's reduced parameter count. Through knowledge distillation, the student model learns to produce similar outputs with fewer parameters, directly addressing the compute and memory constraints of mobile devices
2Productivity
If high capacity NLP models are used to automate data extraction, then extraction performance is improved, but model size and complexity increase
Solution Approach 1:
The student model architecture copies the functional behavior of the teacher model through knowledge distillation. The student model uses a simplified architecture with fewer parameters that can still perform the same extraction tasks, reducing model complexity for mobile deployment
Solution Approach 2:
The patent segments the model into a teacher model for training and a student model for deployment. The student model is trained to replicate the teacher's essential functions while using a more compact architecture, separating the high-performance training phase from the resource-constrained deployment phase
Data Source
AI summary
A computing system generates a plurality of training data sets for generating the NLP model. The computing system trains a teacher network to extract and classify tokens from a document. The training includes a pre-training stage where the teacher network is trained to classify generic data in the plurality of training data sets and a fine-tuning stage where the teacher network is trained to classify targeted data in the plurality of training data sets. The computing system trains a student network to extract and classify tokens from a document by distilling knowledge learned by the teacher network during the fine-tuning stage from the teacher network to the student network. The computing system outputs the NLP model based on the training. The computing system causes the NLP model to be deployed in a remote computing environment.