Topic-considered machine reading understanding model generation method and system

Abstract

The invention discloses a topic-considered machine reading understanding model generation method and system. According to the present invention, the potential topic information in the training sampledata is extracted, and the topic information is utilized to supervise the training of a reading understanding model, so that the effect of the reading understanding model is improved. According to themodel disclosed by the invention, a plurality of topics corresponding to the training samples are extracted before model training, and the topic information of the samples is utilized to improve theeffect of the machine reading understanding work. The basic process of the method comprises the following steps of processing each training sample, and finding out a vector representation capable of representing the sample; clustering the samples, and solving a mean value of the similar sample vectors as the vector representation of the topic; during matching and outputting, using an attention mechanism for representing the higher weight of the words with higher similarity with the topic vector of the sample for the vector. In addition, the training data can obtain a better effect after beingsubjected to better data cleaning, and better topic vector representation can be obtained after noise is reduced.

Description

technical field [0001] The present invention relates to the field of machine reading comprehension in the field of natural language processing, and more specifically, relates to a method and system for generating a machine reading comprehension model considering topics. Background technique [0002] Reading comprehension is the understanding based on reading, which can be abstractly summarized as the process of extracting information from text and understanding meaning through reading. It is a very routine test content in our traditional language disciplines. The general form is to give you an article, and then ask some questions about these articles. Students answer these questions to prove that they understand the main content of the article. , the closer the answer is to the standard answer, the more thorough the understanding of the article is. [0003] Machine Reading Comprehension (Machine Reading Comprehension), as the name suggests, is to let machines replace humans...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is that, when performing machine reading comprehension in the prior art, the machine does not actually understand the meaning of the words and sentences in the paragraph, but only knows which word is more likely to be the beginning word and the sentence of the answer through training. Sentence ending words, which words are more relevant to the technical defects of the question, provide a machine reading comprehension model generation method and system that considers the topic

Images