Recurrent Neural Network Hidden State Generation via Regional Word Vector Aggregation
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Solution Overview
Problem
Existing recurrent neural networks (RNNs) for language processing face challenges in capturing complex linguistic laws due to their reliance on single-space calculations for generating hidden states, which limits their ability to handle variable-length word sequences effectively.
Innovation Solution
The method involves generating regional word vectors of multiple dimensions for a target word vector, combining them, and performing aggregation transformation using a feedforward neural network to obtain an aggregated word vector, which is then used to generate a hidden state, enhancing the network's ability to capture complex linguistic laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-space calculation is used to generate hidden state, then device complexity is reduced, but the ability to capture complex linguistic laws deteriorates
Solution Approach 1:
The patent segments the hidden state generation process into multiple independent calculation spaces (first calculation space for semantic information, second calculation space for syntactic information). Each space processes different aspects of linguistic data separately, then the results are combined to form the final hidden state. This segmentation allows the system to capture complex linguistic laws more effectively while maintaining manageable device complexity.
Solution Approach 2:
The patent introduces multiple calculation spaces as an additional dimension to the traditional single-space RNN architecture. By creating parallel calculation spaces that process different types of information (semantic, syntactic, positional), the system transforms the problem from a single-dimensional calculation to a multi-dimensional processing framework, thereby enhancing the capture of complex linguistic patterns.
2Reliability
If multi-dimensional regional word vectors are generated and aggregated, then the capture rate of complex linguistic laws is improved, but device complexity increases
Solution Approach 1:
The patent divides the word representation into multiple regional word vectors, each capturing different aspects of word information (e.g., semantic region, syntactic region, positional region). These segmented regional vectors are then processed independently in different calculation spaces before being aggregated, which improves linguistic law capture while organizing the complexity into manageable segments.
Solution Approach 2:
The patent merges multiple regional word vectors from different calculation spaces into a unified aggregated word vector. This merging process combines the strengths of each regional representation (semantic, syntactic, positional) to create a comprehensive word embedding that enhances the network's ability to capture complex linguistic laws while consolidating the processing into a unified structure.
3Measurement precision
If aggregated word vector is used to generate hidden state, then processing accuracy for long-distance dependent structures is improved, but computational cost increases
Solution Approach 1:
The patent performs preliminary aggregation of regional word vectors into a comprehensive aggregated word vector before the hidden state generation step. This preliminary action consolidates multiple sources of linguistic information (semantic, syntactic, positional) into a single unified representation in advance, which improves processing accuracy for long-distance dependent structures while avoiding redundant computations during the main RNN processing.
Data Source
AI summary
A method and apparatus for generating a hidden state in a recurrent neural network for language processing are provided. The method includes: generating regional word vectors of at least two dimensions of a target word vector inputted at a first moment; combining the regional word vectors to obtain combined regional word vectors of at least two dimensions; performing aggregation transformation processing on the combined regional word vectors based on a feedforward neural network, to obtain an aggregated word vector corresponding to the target word vector; and generating a target hidden state corresponding to the target word vector based on the aggregated word vector.