Generative Adversarial Network Dialogue Response Diversity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for generating computer responses to user input, such as dialogue and images, are limited in diversity and relevance due to limitations in neural network architectures and methodologies, often producing safe but unresponsive and non-diverse outputs.
Innovation Solution
A hierarchical recurrent encoder-decoder generative adversarial network is employed, which includes a generator and a discriminator, trained using a generative adversarial network to produce diverse and relevant responses by injecting noise during the training stage and utilizing local attention for improved relevance, and word-level classification for better response ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural network architectures are used for generating computer responses, then the system produces safe and stable outputs, but the responses are limited in diversity and relevance
Solution Approach 1:
The patent applies dynamics by making the noise injection mechanism adjustable and controllable during training. The system dynamically adjusts the amount and type of noise injected into the generator input, allowing the model to explore different response patterns while maintaining training stability. This resolves the contradiction by enabling the system to be both stable (through controlled noise) and diverse (through noise-induced variation).
Solution Approach 2:
The patent changes parameters by introducing noise as a controllable parameter in the generator input. By varying noise parameters (amount, type, distribution) during training, the system can generate diverse responses while maintaining reliable training convergence. This parameter change approach allows the model to escape local minima and produce more varied, relevant responses without sacrificing stability.
2Ease of manufacture
If existing neural network methodologies are used, then the training process is simple and straightforward, but the system fails to extract valuable information from large amounts of data
Solution Approach 1:
The patent introduces noise as an intermediary element between the generator input and the neural network processing. This noise intermediary helps the model better extract information from training data by providing additional signal variation that highlights important patterns. The discriminator also serves as an intermediary that provides feedback to improve information extraction, resolving the contradiction between training simplicity and information extraction capability.
3Object-affected harmful factors
If conventional response generation systems are used, then the system maintains safety by producing limited responses, but the responses are not particularly relevant to user input
Solution Approach 1:
The patent implements feedback through the adversarial training mechanism where the discriminator evaluates generated responses and provides gradient feedback to the generator. This feedback loop allows the system to learn what constitutes relevant, informative responses while maintaining safety constraints. The feedback mechanism resolves the contradiction by guiding the generator to produce responses that are both safe (filtered by discriminator) and relevant (learned through iterative feedback).
Data Source
AI summary
Systems and methods for generating responses to user input such as dialogues, and images are discussed. The system may generate, by a response generation module of at least one server, an optimal generated response to the user communication by applying an generative adversarial network. In some embodiments, the generative adversarial network may include a hierarchical recurrent encoder decoder generative adversarial network including a generator and a discriminator component.