Neural Network Loss Function Neighbor Token Dependencies
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Solution Overview
Problem
Conventional neural networks face challenges in processing long input sequences due to the gradient attenuation effect and are limited in recognizing neighbor token dependencies, which hampers their efficiency in sequence labeling tasks such as part-of-speech tagging.
Innovation Solution
The implementation of bi-directional LSTM networks with loss functions that account for neighbor token dependencies, utilizing word-level, character-level, and grammeme-level embeddings, and additional prediction pipelines to compute loss values for current, previous, and next tokens, allowing the network to adjust parameters based on these dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are used for sequence labeling, then the network structure is simple and easy to implement, but the network cannot effectively recognize neighbor token dependencies and suffers from gradient attenuation in long sequences
Solution Approach 1:
The patent segments the loss computation into three distinct components: current token loss, previous token loss, and next token loss. Each component independently evaluates the network's performance on different temporal positions, allowing the model to learn local dependencies without requiring complex architectural changes. This segmentation enables precise measurement of neighbor token relationships while maintaining the simplicity of the underlying neural network structure.
Solution Approach 2:
The patent introduces a temporal dimension to the loss function by considering not just the current token but also previous and next tokens. This transforms the traditional single-point loss evaluation into a multi-point temporal evaluation, enabling the network to capture sequential dependencies without increasing the fundamental network architecture complexity.
2Loss of information
If the network processes long input sequences, then more context information is available, but gradient attenuation occurs reducing training effectiveness
Solution Approach 1:
The patent implements feedback mechanisms by incorporating previous token predictions and next token predictions into the current loss computation. This creates multiple feedback loops where error signals from different temporal positions propagate back to inform weight updates, strengthening gradient flow through long sequences and improving training reliability without sacrificing context information.
3Measurement precision
If traditional loss functions are used that only consider current token, then the computation is simple, but the network cannot capture dependencies between neighboring tokens
Solution Approach 1:
The patent applies local quality by making the loss function sensitive to local temporal relationships. Instead of treating all tokens uniformly, the loss computation is differentiated into three local regions: the current token position, the previous token position, and the next token position. Each region contributes to the overall loss, enabling the network to learn local dependencies while keeping the loss function structure relatively simple and interpretable.
Data Source
AI summary
Systems and methods for neural network training utilizing loss functions reflecting neighbor token dependencies. An example method comprises: receiving a training dataset comprising a plurality of labeled tokens; determining, by a neural network, a first tag associated with a current token processed by the neural network, a second tag associated with a previous token which has been processed by the neural network before processing the current token, and a third tag associated with a next token to be processed by the neural network after processing the current token; computing, for the training dataset, a value of a loss function reflecting a first loss value, a second loss value, and a third loss value, wherein the first loss value is represented by a first difference of the first tag and a first label associated with the current token by the training dataset, wherein the second loss value is represented by a second difference of the second tag and a second label associated with the previous token by the training dataset, and wherein the third loss value is represented by a third difference of the third tag and a third label associated with the next token by the training dataset; and adjusting a parameter of the neural network based on the value of the loss function.