Context-Aware Dialog Generation Using Sentence Embedding and Reinforcement Learning
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Solution Overview
Problem
Automated systems for task-based interactions often lack context and accuracy, leading to increased costs and decreased quality of service as users may need human intervention to resolve issues.
Innovation Solution
A method and system for automated contextual dialog generation using natural language processing techniques, including sentence embedding, context array generation, sequence-to-sequence dialog prediction, and reinforcement learning to provide context-aware responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used for task-based interactions, then service cost is reduced and availability is improved, but accuracy and context understanding deteriorate
Solution Approach 1:
The system performs preliminary actions by maintaining conversation history and context arrays before generating responses. The context array stores embedded representations of previous sentences, enabling the system to access relevant contextual information in advance of response generation, thereby improving accuracy without sacrificing automation efficiency
Solution Approach 2:
The patent introduces sentence embedding models and context arrays as intermediary components between the automated system and user interactions. These intermediaries transform natural language into numerical representations and maintain contextual relationships, enabling the automated system to understand context without requiring human intervention
2Reliability
If context is incorporated into automated responses, then response quality is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of contextual response generation into distinct components: sentence embedding, context array construction, and response generation. Each component handles a specific aspect of context processing, making the overall system more manageable and implementable despite the increased complexity of providing contextual responses
3Measurement precision
If conversation history is stored and processed, then contextual accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system creates compressed numerical copies of conversation history through sentence embedding. Instead of storing and processing the full text of conversation history, the system stores compact vector representations that capture the essential meaning, enabling fast processing while maintaining contextual accuracy
Data Source
AI summary
Systems and method for automated contextual dialog generation for cognitive conversations include embedding a natural language sentence input by a user into a corresponding sentence vector using a sentence embedder. A context array is generated using a contextual sentence embedder to embed the sentence vector and previous sentence vectors of a conversation history into a context array. Response words are predicted from the sentence vector by performing sequence-to-sequence dialog prediction with a dialog prediction network. Context of the input sentence is quantified by extracting context features from the context array using a situation quantification network. A response dialog is generated in natural language to display to a user, the response dialog responding to the input sentence with a response generator by determining a dialog state including the response words and the quantified context and optimizing the response dialog with reinforcement learning corresponding to the dialog state.