Training Data Enhancement for Interactive Response Systems
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Solution Overview
Problem
Chatbot systems face challenges in maintaining differentiated training data while optimizing utterance routing and improving generalizability and reducing confusability, as existing methods fail to effectively refine intents and entities based on user interactions.
Innovation Solution
A processor-enhanced method that iteratively determines root verbs and entities from training data, performs pairwise similarity analysis to create new intents, and generates new training data by refining intents and entities, thereby enhancing the chatbot's ability to understand user queries and improve response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing training data is used without refinement, then the chatbot can maintain simple training data structure, but the chatbot fails to accurately route user queries and has poor generalizability
Solution Approach 1:
The patent segments training data into distinct intents and entities, where each intent represents a specific user goal and entities represent key information elements. This segmentation allows the chatbot to systematically categorize and route queries by matching intents and extracting entities, improving routing accuracy while maintaining an organized data structure that is manageable through automated processing.
Solution Approach 2:
The patent performs preliminary analysis of training data to automatically identify and extract intents and entities before the chatbot deployment. By pre-processing the training data to establish intent-entity relationships, the system prepares the data structure in advance, which improves query routing accuracy without requiring complex real-time processing during actual user interactions.
2Adaptability or versatility
If training data is refined through iterative analysis, then the chatbot improves generalizability and reduces confusability, but the processing time and computational resources increase
Solution Approach 1:
The patent implements iterative analysis where the system processes training data, evaluates the results, and refines intent and entity extraction based on performance feedback. This feedback loop continues until convergence or maximum iterations, automatically improving generalizability by learning from previous analysis rounds without requiring manual intervention, thus balancing processing time with quality improvement.
Solution Approach 2:
The system performs self-service by automatically analyzing and refining its own training data without external assistance. The iterative process autonomously identifies patterns, extracts intents and entities, and improves the data structure through multiple passes, reducing the need for manual data curation while achieving high generalizability through systematic self-improvement.
3Manufacturing precision
If manual refinement of intents and entities is performed, then the training data quality improves, but the ease of operation and maintenance decreases
Solution Approach 1:
The system automatically performs intent and entity extraction and refinement without requiring manual intervention. By implementing self-service capabilities that autonomously analyze training data, generate intent definitions, and identify entities, the system maintains high data quality while eliminating the operational burden of manual curation, thus improving ease of maintenance.
Solution Approach 2:
The patent replaces manual mechanical processes of intent identification and entity extraction with automated computational methods. Machine learning algorithms and natural language processing techniques substitute human analysts, automatically refining training data quality through systematic analysis while significantly reducing the operational effort and time required for data maintenance.
Data Source
AI summary
In an approach for training data enhancement for an interactive response system, a processor retrieves a set of training data including a set of intents, a set of entities, and a set of utterances that map to each intent. A processor determines iteratively a root verb among the set of utterances for each intent. A processor to determine a set of new intents based on analysis of the determined root verb by performing a pairwise iteration and similarity score over the set of intents. A processor determines iteratively one or more new entities for each new intent. A processor generates a set of new training data based on the set of new intents and entities.