Feature-Augmented Neural Networks for Context-Aware Language Processing
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Solution Overview
Problem
Existing language models often fail to effectively incorporate context into their outputs, leading to inconsistent results when applied in different scenarios, despite some models attempting to account for context to varying extents.
Innovation Solution
A neural network system that uses an input vector and a feature vector to generate output information, where the feature vector is derived from either latent information within the input or external sources, enhancing the model's contextual understanding through techniques like Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Principal Component Analysis (PCA).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple topic-specific models are used for different contexts, then context accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal language model that can handle multiple contexts through dynamic context embedding rather than requiring separate models for each topic. The system uses a single model architecture that adapts to different contexts by injecting context-specific features into the input representation, making the model multi-functional without increasing structural complexity
Solution Approach 2:
The system changes the parameters (context embeddings) dynamically based on the input context rather than changing the model structure itself. By modifying the input parameters to include context information, the same model can accurately handle different topics and contexts, resolving the contradiction between accuracy and complexity
2Reliability
If context information is incorporated into language models, then output relevance is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary action by pre-computing and storing context embeddings that can be quickly retrieved and injected during inference. By preparing context representations in advance, the system reduces the computational overhead during actual language model processing while maintaining output relevance
Solution Approach 2:
The patent introduces context embeddings as an intermediary representation that bridges the input context and the language model processing. This intermediary layer allows context information to be incorporated efficiently without directly increasing the computational complexity of the core language model operations
Data Source
AI summary
A system is described herein which uses a neural network having an input layer that accepts an input vector and a feature vector. The input vector represents at least part of input information, such as, but not limited to, a word or phrase in a sequence of input words. The feature vector provides supplemental information pertaining to the input information. The neural network produces an output vector based on the input vector and the feature vector. In one implementation, the neural network is a recurrent neural network. Also described herein are various applications of the system, including a machine translation application.