Confidence Evaluation Method and Device Applicable to Grammatical Analysis

Abstract

The invention discloses a confidence degree evaluation method and device suitable for grammatical analysis, wherein the method includes the following steps: acquiring training data, and training a translation model from logical form to natural language according to the training data; capturing the logical form through the translation model The mapping relationship with natural language, obtain the natural semantics of the input data and the predicted analytical results, and estimate the data uncertainty; obtain the model uncertainty of each output result through the confidence estimation algorithm, where, for the data Uncertainty, the posterior probability output of the basic model is connected with the confidence feature, and its abstract representation is obtained through the encoder, and the conversion probability of the label is learned to obtain the confidence of the input data. This method can effectively improve the prediction accuracy of the model, and can give the interpretation of the prediction results in actual scenarios.

Description

technical field [0001] The invention relates to the technical field of grammatical analysis, in particular to a confidence evaluation method and device suitable for grammatical analysis. Background technique [0002] Today, with the rapid development of the Internet, it is the era of information and big data domination. At the same time, the demand for information visualization is increasing day by day, and data visualization provides a new method of information display. Therefore, a large number of hidden information can be discovered through data visualization technology. rules to provide a basis for decision-making. Most of the currently commonly used data visualization systems are based on a specific scene or a certain form of expression, and there is no general data visualization view generation method for information classification. [0003] Related technologies, (1) A method and system for acquiring confidence in face recognition. The method utilizes obtaining the f...

AI Technical Summary

Problems solved by technology

[0004] However, most of the existing confidence measurement methods are based on specific domains, such as images, etc.

Confidence measures established for specialized domains somewhat limit their scope of application

At the same time, most of the existing methods need another set of data storage for comparison, and finally give the uncertainty of the model results

This method does not specifically point out which part affects the effect of model confidence, and still cannot explain the reasons for the uncertainty of the model.

[0005] At the same time, most of the current confidence measurement methods are based on the posterior probability. However, due to the nonlinearity and complexity of the neural network, it is difficult for this method to accurately reflect the confidence level of the model. Therefore, a more comprehensive measurement method is needed to analyze The reasons for model uncertainty, and use model uncertainty to improve model results

[0006] In addition, most of the existing confidence estimation systems are based on statistical posterior probability methods, and do not take into account whether the input data conforms to the original data distribution. This method leads to incomplete confidence estimation, and in nonlinear environments cannot reliably give probability values ​​in

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