Bayes-based open answer decision method

Abstract

The invention discloses a Bayes-based open answer decision method and belongs to the technical field of computer programs. A worker quality model is established by use of past performance of workers.The method comprises a prior probability preprocessing step, wherein blank-filling content is preprocessed, and after the number of candidate answers is increased due to addition of the blank-fillingcontent, the prior probabilities of all the candidate answers need to be recalculated to ensure that an answer decision is more accurate; a Bayes decision step, wherein an answer decision algorithm ofmultiple-choice questions is optimized, and an approximation algorithm with low time complexity is given; and after answers and quality of the workers are received, the candidate answers to the questions and the prior probabilities are preprocessed first, a Bayes probability model is established according to the answers of the workers after processing results are obtained, and posterior probability distribution of the candidate answers to the questions is obtained; and a worker quality model updating step, wherein the worker quality model is updated dynamically along with the change of the quantity of answered questions of the workers, and an updated worker quantity model is established to ensure that the quality of the workers is credible in the answer decision process.

Description

technical field [0001] The invention relates to a Bayesian-based open answer decision-making method, which belongs to the technical field of computer programs. Background technique [0002] In real life, there are often some problems that are difficult or ineffective for computers to handle, such as labeling images, measuring whether two records are the same entity, evaluating a product, and so on. Such problems that are difficult for machines to handle can be done with the help of crowdsourcing. Crowdsourcing directly publishes tasks to the Internet, and solves problems that traditional computers cannot handle alone by gathering unknown masses on the Internet. [0003] Since workers come from different regions, ages and cultural backgrounds are not the same, it is difficult to guarantee the quality of answering questions. The task issuer of the crowdsourcing platform hopes to get a more credible answer through redundancy, so they will issue the task to multiple workers to...

AI Technical Summary

Problems solved by technology

[0006] (1) The accuracy of workers answering questions is constantly changing. In the past, the size of the quality model matrix established for workers is fixed, which is only applicable to the case where the candidate answers are fixed.

[0007] (2) The current answer decision-making method is based on a single task type, and lacks a decision-making method that is compatible with multiple task types and can handle open answers, and the accuracy and efficiency of decision-making need to be improved

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