Decoder Beam-Score Calibration for Logical-Form Confidence
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Solution Overview
Problem
Existing natural language interfaces for databases require extensive knowledge of database management languages and struggle with the need for large amounts of labeled training data, leading to inefficiencies in translating natural language queries to machine-oriented languages like SQL, and there is a lack of effective confidence scoring for model outputs.
Innovation Solution
A machine-learning technique that calibrates confidence scores for a machine learning model translating natural language to a meaning representation language like Oracle Meaning Representation Language (OMRL) by using raw beam scores from a decoder's beam levels and a calibration model to predict the correctness of logical forms, providing a conditional probability score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained to translate natural language to logical form, then the translation capability is improved, but the model lacks reliable confidence scoring for its outputs
Solution Approach 1:
The patent introduces a calibration model as an intermediary component that takes raw beam scores from the decoder and transforms them into calibrated confidence scores. This calibration model acts as a mediator between the translation model's raw outputs and the final confidence assessments, improving the reliability of confidence scoring without modifying the core translation capability.
Solution Approach 2:
The patent applies parameter transformation by converting raw beam scores (which are not directly interpretable as confidence levels) into calibrated confidence scores through the calibration model. This parameter change enables the system to provide meaningful confidence measurements that accurately reflect the model's uncertainty about its translations.
2Measurement precision
If extensive labeled training data is used to train the machine learning model, then the translation accuracy is improved, but the data requirements and training complexity increase
Solution Approach 1:
The patent performs preliminary calibration of the model's confidence scores using a separate calibration dataset before deployment. This preliminary action allows the system to adjust and refine confidence score predictions without requiring extensive retraining on large labeled datasets, thereby reducing data requirements while maintaining translation accuracy.
Solution Approach 2:
The calibration model creates a simplified representation of confidence score relationships based on training data patterns. Instead of requiring the main translation model to learn all aspects from extensive labeled data, the calibration model copies and adapts confidence patterns from a smaller calibration dataset, reducing the overall data burden.
3Productivity
If raw beam scores are used directly as confidence scores, then the computation is simple, but the confidence scores are not well-calibrated and misleading
Solution Approach 1:
The calibration model serves as an intermediary layer between raw beam scores and final confidence scores. While this adds a computational step, it maintains efficiency by using a relatively simple calibration model that can be trained once and then applied quickly during inference, achieving well-calibrated scores without excessive computational overhead.
Solution Approach 2:
The system transforms raw beam scores into calibrated confidence scores through parameter transformation in the calibration model. This parameter change improves measurement precision by making the scores properly calibrated while maintaining reasonable computational efficiency through the use of efficient calibration algorithms.
Data Source
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.