On-Device Neural Grammar Checking for Privacy and Low Latency
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Solution Overview
Problem
Existing grammar checking technologies require significant computing resources and send user data to external systems, compromising privacy and performance.
Innovation Solution
Implementing on-device neural networks for grammar checking on computing devices, allowing local processing of text without sending data to the cloud, and continuously improving the neural networks for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-side grammar checking is used, then grammar checking accuracy can be improved, but user data privacy is compromised and network resources are consumed
Solution Approach 1:
The grammar checking functionality is extracted from the server environment and embedded directly into the mobile device through a neural network model. This extraction allows the system to maintain high accuracy while processing data locally, eliminating the need to transmit user data to external servers and thus preserving data privacy.
Solution Approach 2:
A simplified version of the server-side grammar checking capability is copied to the mobile device in the form of a trained neural network model. This copied model reproduces the essential grammar checking functionality with sufficient accuracy for mobile use, enabling local processing without requiring continuous network connectivity.
2Measurement precision
If server-side grammar checking is used, then comprehensive grammar analysis can be achieved, but network bandwidth is consumed and response time increases
Solution Approach 1:
The grammar checking processing capability is extracted from the network environment and relocated to the mobile device. This extraction eliminates the need for network communication during grammar checking operations, thereby conserving network bandwidth and reducing energy consumption associated with data transmission.
3Object-affected harmful factors
If on-device neural networks are used, then data privacy is preserved and network usage is reduced, but device computing resources are consumed
Solution Approach 1:
A compact neural network model is copied to the mobile device, optimized for mobile hardware constraints. This copied model performs grammar checking locally, preserving data privacy while consuming minimal device resources compared to running full-featured server-side models.
Solution Approach 2:
The neural network model is optimized with specific parameters suited for mobile deployment, including reduced model size, quantized weights, and pruning of unnecessary connections. These parameter changes enable the model to run efficiently on mobile hardware with limited processing power and memory.
4Measurement precision
If continuous neural network updates are performed, then grammar checking accuracy improves, but device storage and processing overhead increases
Solution Approach 1:
The neural network model undergoes parameter optimization through techniques such as quantization (reducing precision from 32-bit to 8-bit integers), pruning (removing redundant connections), and knowledge distillation (compressing a large model into a smaller version). These parameter changes reduce model size and storage requirements while maintaining acceptable accuracy levels.
Data Source
AI summary
A computing device may receive inputted text and perform, using one or more neural networks, on-device grammar checking of a sequence of words in the inputted text, including determining, using the one or more neural networks, a grammatically correct version of the sequence of words and determining that the sequence of words does not match the grammatically correct version of the sequence of words. The computing device may, in response to determining that the sequence of words does not match the grammatically correct version of the sequence of words, output, for display at a display device, at least a portion of the grammatically correct version of the sequence of words as a suggested replacement for at least a sequence of the sequence of words in the inputted text.