Latent Variable Segmentation for Neural Network Response Generation
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Solution Overview
Problem
Current natural language generation technologies using neural networks face challenges in generating accurate and diverse responses to input utterances, particularly in capturing context and intent, leading to imbalances in accuracy and diversity.
Innovation Solution
A processor-implemented method and apparatus that generates latent variables from embedding vectors to determine attention information, using both sentence-level and word-level latent variables to output natural language responses. This involves extending embedding vectors to a predefined vector space, calculating attention information through neural networks, and decoding responses based on sampled embedding vectors and attention information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model is trained to generate natural language responses, then the accuracy of responses is improved, but the diversity of responses deteriorates
Solution Approach 1:
The patent segments the latent variable into two independent components: a sentence-level latent variable capturing overall context and intent, and word-level latent variables capturing specific word choices. This segmentation allows the model to maintain diverse word selections while preserving accurate contextual understanding, resolving the contradiction between accuracy and diversity.
Solution Approach 2:
The patent transitions from a single-dimensional latent variable to a multi-dimensional latent variable structure by adding word-level latent variables on top of the sentence-level latent variable. This dimensional expansion enables the model to independently control different aspects of generation (context accuracy vs. word diversity), allowing simultaneous optimization of both accuracy and diversity.
2Measurement precision
If attention mechanism is strengthened to capture context, then the accuracy of context understanding is improved, but the complexity of the model deteriorates
Solution Approach 1:
The attention mechanism is segmented into two independent attention components: sentence-level attention for capturing overall context and intent, and word-level attention for capturing specific word relationships. This segmentation allows the model to understand context accurately without requiring a single overly complex attention mechanism, reducing overall model complexity while maintaining accuracy.
3Measurement precision
If latent variable is extended to capture more context, then the accuracy of intent capture is improved, but the computational cost deteriorates
Solution Approach 1:
The latent variable is segmented into sentence-level and word-level components, allowing the model to capture intent accurately through the sentence-level variable while using computationally efficient word-level variables for fine-grained details. This segmentation reduces the computational burden compared to a single comprehensive latent variable that would need to capture all contextual information simultaneously.
Data Source
AI summary
Provided is a processor-implemented method of generating a natural language, the method including generating a latent variable from an embedding vector that corresponds to an input utterance, determining attention information related to the input utterance by applying the generated latent variable to a neural network model, and outputting a natural language response that corresponds to the input utterance based on the calculated attention information.