Context Vector Encoding for Neural Network Sequence Representation
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Solution Overview
Problem
Existing neural network models, particularly those using self-attention mechanisms, suffer from weakened network representation due to limited consideration of relationships between elements in a discrete sequence, leading to degraded performance in tasks like machine translation.
Innovation Solution
The method involves obtaining a context vector related to a discrete sequence to encode it, thereby strengthening the dependence between elements, using this context vector to determine query and key vectors, and calculating logical similarity to enhance the encoding and decoding processes in text translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If attention weight is calculated for each element in discrete sequence using direct connection between hidden states, then calculation simplicity is maintained, but network representation capability deteriorates
Solution Approach 1:
The patent introduces a context vector as an intermediary between the discrete sequence elements and the attention mechanism. This context vector aggregates information from the entire sequence, allowing elements to interact through this mediator rather than through direct connections alone, thereby enhancing network representation capability while maintaining computational feasibility
Solution Approach 2:
The patent adds a new dimension to the attention mechanism by incorporating the context vector dimension. Instead of only considering relationships between pairs of elements in the original sequence space, the method projects elements into an extended space that includes context information, enabling more comprehensive dependency modeling
2Productivity
If only relationship between two elements is considered in dependence calculation, then computational efficiency is maintained, but learning capability deteriorates
Solution Approach 1:
The patent performs preliminary computation by calculating the context vector from the entire discrete sequence before the attention mechanism processes individual element pairs. This pre-aggregation of sequence-level information enables subsequent attention calculations to leverage global context without performing expensive full-sequence computations for each pair
Solution Approach 2:
The patent merges local pairwise relationships with global sequence context by combining the attention weights (capturing local element relationships) with the context vector (capturing global sequence information). This fusion allows the model to simultaneously consider both fine-grained element interactions and overall sequence patterns
Data Source
AI summary
Embodiments of this disclosure disclose an information processing method, apparatus and a non-transitory computer readable medium. The method includes: obtaining a target text sequence corresponding to to-be-processed text information; obtaining a context vector according to the target text sequence; determining a logical similarity corresponding to the target text sequence according to the context vector and the target text sequence; and encoding the target text sequence corresponding to target text information by using the logical similarity to obtain a text encoding result. In this embodiment of this disclosure, a context vector related to a discrete sequence is used to encode the discrete sequence, to strengthen the dependence between elements in the discrete sequence, thereby enhancing the performance of a neural network model and improving the learning capability of the model.