NL-to-Query Routing for Out-of-Scope Utterance Detection
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately detect out-of-domain and out-of-scope utterances when converting user inputs to database queries, particularly due to the need for large amounts of labeled training data and the inability to handle ambiguous or unclear queries, leading to inefficiencies and incorrect outputs.
Innovation Solution
A computer-implemented method involving a first machine learning model that generates a score indicating whether a natural language utterance is translatable to a database query, using a second model for conversion, and includes training data to identify out-of-domain and out-of-scope utterances, with a system architecture that routes unclear queries to a master bot for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a natural language processing system uses a single model for direct conversion without detection, then the system complexity is reduced, but the accuracy of detecting out-of-domain and out-of-scope utterances deteriorates
Solution Approach 1:
The system divides the natural language processing task into two separate models: a detection model that identifies out-of-domain and out-of-scope utterances, and a conversion model that translates valid utterances to logical form. This segmentation allows each model to specialize in its specific function, improving detection accuracy while maintaining reasonable system complexity through modular architecture.
2Measurement precision
If the system routes all utterances through the detection model first, then detection accuracy improves, but the processing time increases
Solution Approach 1:
The detection model performs preliminary filtering of utterances before they reach the conversion model. By identifying and filtering out out-of-domain and out-of-scope utterances in advance, the system avoids wasting processing time on invalid inputs, thereby reducing overall processing time while maintaining high detection accuracy.
3Manufacturing precision
If the system uses domain-specific training data, then the conversion accuracy for domain tasks improves, but the adaptability to new domains deteriorates
Solution Approach 1:
The detection model is trained with multi-domain training data that includes various domains and topics, enabling it to function universally across different domains. This universal detection capability allows the system to maintain high conversion accuracy within specific domains while simultaneously adapting to new domains without requiring domain-specific detection models.
Data Source
AI summary
Systems and methods identify whether an input utterance is suitable for providing to a machine learning model configured to generate a query for a database. Techniques include generating an input string by concatenating a natural language utterance with a database schema representation for a database; providing the input string to a first machine learning model; based on the input string, generating, by the first machine learning model, a score indicating whether the natural language utterance is translatable to a database query for the database and should be routed to a second machine learning model, the second machine learning model configured to generate a query for the database based on the natural language utterance; comparing the score to a threshold value; and responsive to determining that the score exceeds the threshold value, providing the natural language utterance or the input string to the second machine learning model.