Cascaded Sentence Encoding Model for Gradient Error Stabilization
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Solution Overview
Problem
Current sentence processing models, particularly in machine translation, suffer from low accuracy due to inadequate weight training methods, where the gradient of the error decreases exponentially, leading to inaccurate weights and reduced efficiency.
Innovation Solution
The proposed solution involves a sentence processing method using a cascaded encoding model with linear and non-linear operations in each processing node, generating sentence vectors for decoding, and a decoding model that performs linear and non-linear operations on query states and contexts to improve weight accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for sentence processing, then the model structure is simple, but the processing accuracy is low due to gradient error decreasing exponentially
Solution Approach 1:
The model segments the sentence processing into multiple processing nodes (first processing node, second processing node, etc.), where each node independently processes different aspects of the sentence. This segmentation allows gradient flow through multiple pathways, preventing the exponential decrease of gradient error and improving both accuracy and training stability.
Solution Approach 2:
The patent introduces intermediate processing nodes that act as mediators between the input sentence and the final output. These intermediate nodes maintain gradient flow and provide stable weight training by breaking down the direct mapping into multiple controlled transformations, thereby improving reliability while enhancing processing accuracy.
2Measurement precision
If more processing nodes are added to improve accuracy, then the processing accuracy increases, but the model complexity increases
Solution Approach 1:
The model divides the processing into multiple nodes, but each node uses a standardized structure with consistent operations. This segmentation improves accuracy through multiple processing stages while controlling complexity by reusing the same operational patterns across nodes, making the increased complexity manageable and systematic.
Solution Approach 2:
The patent adjusts parameters such as the number of processing nodes and the types of operations performed at each node to optimize the balance between accuracy and complexity. By carefully controlling these parameters, the model achieves higher accuracy without excessive complexity growth.
3Productivity
If traditional encoding methods are used, then the model structure is simple, but the weight training efficiency is low
Solution Approach 1:
The multiple processing nodes operate in sequence, maintaining continuous useful action throughout the weight training process. Each node contributes to the final accuracy while the continuous processing ensures efficient utilization of computational resources, improving both training efficiency and weight accuracy simultaneously.
Data Source
AI summary
Embodiments of this disclosure disclose a sentence processing method and device. The method may include performing word segmentation operation on a source sentence to be encoded to obtain m words. The method may further include obtaining an ith word in the m words using an ith encoding processing node in the n encoding processing nodes, and obtaining an (i−1)th word vector from an (i−1)th encoding processing node. The method may further include performing linear operation and non-linear operation on the ith word and the (i−1)th word vector using the first unit of the ith encoding processing node to obtain an ith operation result, and outputting the ith operation result to the at least one second unit for processing to obtain an ith word vector. The method may further include generating, in response to obtaining m word vectors, a sentence vector according to the m word vectors.