The invention discloses an unsupervised machine reading understanding method based on large-scale problem self-learning. The method comprises the following steps: firstly, dividing data into four types; and then, carrying out the following steps: S1, training unlabeled general data by using a standard pre-training model to obtain a pre-training language model; s2, training the marked general data by using a pre-training language model to obtain a question generator, and generating a specific task general domain model; s3, generating synthesized intra-domain data from the unlabeled intra-domain data by using a problem generator, filtering by using a specific task general domain model, and training a high-quality synthesized intra-domain data set obtained by filtering to obtain a new pre-training model; s4, mixing the marked intra-domain data through a low-quality synthetic data set obtained by filtering, marking answers, and then training by using a new pre-training model to obtain a final model; and based on the final model, inputting data to obtain a machine reading understanding result.
Unsupervised machine reading understanding method based on large-scale problem self-learning