Neural Network Entity Memory for Sequence Generation
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Solution Overview
Problem
Conventional neural network systems for generating output sequences struggle with maintaining consistent entity representations, leading to inconsistent entity and attribute sets over long output sequences, which results in reduced performance and increased computational resources.
Innovation Solution
The system employs a neural network with dual layers, including a self-attention block and an entity-aware block, to maintain entity memory data, allowing for consistent entity and attribute representation throughout the output sequence, reducing the need for extensive training and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional neural networks generate output sequences without entity memory data, then the system is simpler and requires fewer computational resources, but entity consistency deteriorates over long output sequences
Solution Approach 1:
The system segments entity information from general sequence data and stores it separately in entity memory data structures. Each entity is represented as a distinct object with its own attributes, allowing the neural network to maintain consistent entity representations independently of the overall sequence generation process.
Solution Approach 2:
The patent introduces an additional dimension of entity memory data alongside the standard sequence processing. This extra dimension allows the system to track and maintain entity consistency across long sequences without increasing the complexity of the core neural network architecture.
2Reliability
If conventional neural networks use more layers to improve entity representation, then performance improves, but computational resources and memory footprint increase
Solution Approach 1:
The system performs preliminary extraction and organization of entity information before the main sequence generation process. By pre-processing and storing entity data in structured memory formats, the neural network can efficiently access consistent entity representations during generation without requiring additional processing layers.
Solution Approach 2:
Entity memory data acts as an intermediary between the input sequence and the output generation process. This intermediate structure provides consistent entity representations to the neural network, improving performance without requiring the network itself to be more complex or computationally intensive.
3Manufacturing precision
If the neural network processes only input sequences without designated entities, then the system is simpler to operate, but entity attribute consistency deteriorates
Solution Approach 1:
The system automatically identifies and extracts entity information from the input sequence, organizing it into structured entity memory data without requiring manual intervention. This self-service approach maintains attribute consistency while keeping the system easy to operate, as the entity extraction and organization happens automatically during processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences using entity memory data. In particular, a neural network is used to generate an output sequence conditioned on an input sequence and on the entity memory data.