Neural Network NLG Template Selection for Voice User Personalization
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Solution Overview
Problem
Current natural language generation (NLG) systems struggle to adapt to individual user styles and preferences, leading to suboptimal user experiences in voice-controlled devices, as they often rely on static templates that do not account for user-specific communication goals and speech characteristics.
Innovation Solution
A method that uses a neural network NLG template selection process to determine whether to use a static or dynamic NLG template, with the latter being generated based on user history, profile, and speech characteristics, allowing for personalized NLG outputs tailored to each user's communication style and intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static NLG templates are used, then system complexity is reduced and processing speed is improved, but adaptability to user-specific communication styles deteriorates
Solution Approach 1:
The system dynamically selects between static and dynamic NLG templates based on user characteristics and communication context. A neural network analyzes user speech patterns, history, and preferences to determine whether to use pre-defined static templates or generate personalized dynamic templates, allowing the system to adapt its complexity level to each interaction scenario.
Solution Approach 2:
The system changes the parameter of template flexibility by switching between fixed static templates and customizable dynamic templates. When user-specific adaptation is needed, the system transitions to dynamic templates that can be customized with user profile parameters, speech style parameters, and communication goal parameters, thereby improving adaptability without permanently increasing system complexity.
2Adaptability or versatility
If dynamic NLG templates are generated, then adaptability to user preferences is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user speech to extract communication goals, styles, and preferences before template selection. User profiles and communication patterns are pre-analyzed and stored, so that during actual NLG generation, the system can quickly retrieve relevant user characteristics and make rapid template selection or generation decisions, reducing real-time processing time.
Solution Approach 2:
The system applies partial personalization by using dynamic template generation only when necessary based on user complexity preferences. For users or situations where high personalization is not required, the system uses lighter static templates, thereby avoiding unnecessary computational overhead and processing time while still providing adequate adaptability when needed.
3Ease of operation
If user-specific NLG outputs are generated, then user experience is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the NLG generation process into distinct modules: user identification module, communication goal extraction module, style analysis module, template selection module, and template generation module. Each module handles a specific aspect of user personalization, allowing the system to provide comprehensive user-specific NLG outputs while managing computational complexity through modular architecture and targeted processing.
Data Source
AI summary
A system and method of generating a natural language generation (NLG) output, wherein the method includes: receiving speech signals from a user at a microphone of a client device; determining a requested communication goal and at least one inputted communication value based on the received speech signals; determining to use a static natural language generation (NLG) template or a dynamic NLG template to generate an NLG output, wherein the determination of whether to use a static NLG template or a dynamic NLG template is made using a neural network NLG template selection process; selecting an NLG template after the determination of whether to use a static NLG template or a dynamic NLG template; and generating an NLG output based on the selected NLG template.