Natural Language Interpretation Model Self-Service Learning
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Solution Overview
Problem
Natural language interpretation models often fail to accurately interpret user intent in queries, leading to modifications by users to achieve desired results, as they do not fully understand the nuances of human language semantics and syntax.
Innovation Solution
A system that discovers modifications made to query results by users and identifies changes to the natural language interpretation model to generate more accurate results, allowing the model to learn from user edits and adjust its interpretation over time, with evidence thresholds determining the extent of changes based on confidence and user or group-specific language usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a natural language interpretation model is used to convert natural language queries into structured queries, then ease of operation is improved, but accuracy of query results deteriorates due to inability to fully understand human language nuances
Solution Approach 1:
The system implements a feedback mechanism where user modifications to query results are automatically detected and used to train the natural language interpretation model. The model learns from user edits to understand nuances better, progressively improving accuracy while maintaining ease of natural language query formulation.
Solution Approach 2:
The system enables self-service learning where the interpretation model automatically improves itself by analyzing user interactions with query results. Users don't need to manually retrain the model; their natural editing behavior provides the training data, allowing the model to serve itself through continuous improvement.
2Measurement precision
If the natural language interpretation model is modified continuously based on user observations, then accuracy of query results is improved, but device complexity increases due to iterative learning processes
Solution Approach 1:
The system automates the model modification process through self-service mechanisms. The interpretation model automatically analyzes user modifications, identifies learning opportunities, and updates itself without requiring complex manual intervention or external training pipelines, thereby managing complexity while improving accuracy.
Solution Approach 2:
The system modifies the interpretation model by changing its internal parameters and learning rules based on observed user behavior patterns. Through parameter updates derived from user edits, the model adapts to different query contexts and nuances, improving accuracy through controlled parameter changes rather than complex structural modifications.
Data Source
AI summary
The modifying of a natural language interpretation model for interpreting natural language queries. The system discovers modifications that one or more queriers made to one or more original query results of one or more natural language queries to generate one or more modified query results. The system then uses the discoveries to identify one or more changes to a natural language interpretation model that would result (given the same natural language queries) in one or more query results that more accurately reflect the one or more modified query results. The system the causes the natural language interpretation model to be modified with at least one of the one or more identified changes. Accordingly, over time, the natural language interpretation model may learn from observations of its own performance.