Candidate answer screening method for global machine reading comprehension modeling

Abstract

The invention provides a candidate answer screening method for global machine reading comprehension modeling, which belongs to the technical field of computer information screening. All the paragraphscorresponding to a question are taken as a candidate answer fragment locating range in the method. Firstly, the F1 values between the text fragments of paragraphs are obtained, and the best candidateanswer fragment is selected by using F1; and on the other hand, after the features between the paragraphs and the question are extracted, correlation scoring is carried out by using a logistic regression model, a selected candidate answer paragraph set is obtained according to the scores, whether the paragraph where the best candidate answer fragment is located is in the candidate answer paragraph set is determined, and the paragraph where the best candidate answer fragment is located is forcibly put in the first place of the candidate answer paragraph set. Finally, the best candidate answerfragment and the candidate answer paragraph set are output. The method has the advantage that the efficiency of training and prediction is improved.

Description

technical field [0001] The invention relates to a method for screening candidate answers in global-oriented machine reading comprehension modeling, and belongs to the technical field of computer information screening. Background technique [0002] Large-scale datasets play an extremely important role in advancing a field of research. Several datasets have also been released in the field of machine reading comprehension, which has greatly facilitated research in this area. For example, for the SQuAD dataset, several machine reading comprehension models have outperformed human annotations. The largest data set in Chinese is DuReader, which is a large-scale human-annotated reading comprehension data set for the open field of the real world. The questions and passages in it are collected from search engines, and the answers are all artificial. label. [0003] In reading comprehension tasks, for a question, there may be multiple articles that can provide the necessary answer i...

AI Technical Summary

Problems solved by technology

[0004] Obviously, the existing answer paragraph screening method is a local greedy method, which cannot deal with the phenomenon that multiple paragraphs in a chapter are related to the question, and will generate too many or too few candidate answers due to the influence of the number of chapters

However, the selection criteria of candidate answer fragments are not consistent with the selection of candidate answer paragraphs, and the best candidate answer fragments with better F1 values ​​may be missed

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