NL-to-Logical-Form Confidence Calibration Using Beam Scores

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Solution Overview

Problem

Existing natural language to logical form translation systems, such as NL2SQL, require large amounts of labeled data for deep learning and struggle with model uncertainty, making it difficult to interpret the confidence of the output logical forms, especially in chatbots interacting with databases.

Innovation Solution

A confidence score calibration model is developed that utilizes raw beam scores from a decoder's beam levels to classify the correctness of logical forms, using a calibration model to predict a confidence score based on heuristic search algorithms, enabling better interpretation and understanding of model uncertainty.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If deep learning approaches are used to convert natural language queries to SQL queries, then the system can generalize across tasks and domains, but it requires a large amount of labeled training data which is challenging to obtain

Engineering Contradiction:

Improvegeneralization capabilityVSAvoidlabeled training data

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent introduces an intermediary component (semantic parser or translation layer) that bridges natural language and SQL, using pre-defined schemas and templates to reduce the need for extensive labeled data while maintaining generalization capability across different domains and tasks

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of manufacture

If rule-based systems are used to translate natural language sentences to SQL queries, then the system is domain-specific and follows fixed rules, but it does not generalize well to new use cases

Engineering Contradiction:

Improvesystem implementabilityVSAvoidgeneralization to new use cases

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements a dynamic system that combines rule-based translation with machine learning components, allowing the system to adapt to new domains and use cases by learning from data while maintaining the structured approach of rule-based systems for core functionality

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs preliminary action by pre-defining SQL schemas, templates, and translation rules that can be applied to new domains without requiring complete re-engineering, enabling faster adaptation to new use cases while maintaining systematic structure

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If statistical-based slot-filling systems are used to parse natural language sentences, then the system can label tokens according to their semantic role, but it has limitations on the types of sentences that can be parsed

Engineering Contradiction:

Improvesemantic role identificationVSAvoidsentence type coverage

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal parsing framework that combines statistical slot-filling with more flexible semantic parsing techniques, enabling the system to handle multiple sentence types and structures while maintaining precise semantic role identification through a unified approach

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Ease of operation

If a chatbot uses natural language interface to interact with database systems, then users can query databases with plain language without knowing SQL syntax, but the system struggles with model uncertainty and difficulty in interpreting confidence of output logical forms

Engineering Contradiction:

Improveuser interaction simplicityVSAvoidmodel confidence interpretation

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent implements feedback mechanisms that provide confidence scores and uncertainty measurements to the chatbot system, allowing it to assess the reliability of its generated SQL queries and provide feedback to users or developers when confidence is low, thereby improving trust and reliability while maintaining ease of operation

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250378073A1Calibrating confidence scores of a machine learning model trained as a natural language interface

Publication Date: 2025.12.11 ORACLE INT CORP

AI summary

Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.