A machine reading method, a system, a device, and a storage medium. The method comprises: obtaining word vectors by means of a glove word vector training model; obtaining field embeddings by using a character-level convolutional neural network; splicing the word vectors and the field embeddings to form an input vector; performing feature extraction on the input vector by means of lstm and a transformer to obtain a text having local features and an overall feature; processing the obtained text by means of a Bidirectional Attention Flow model and the transformer to obtain all association information of a question and an answer in the text; and processing all the association information of the question and the answer in the obtained text by means of lstm, and taking a sentence having the highest probability as the answer to the question in the text. According to the method, the transformer model and the lstm model are mutually combined to form a new machine reading network structure, solving the problem that the overall correlation and the local correlation of sentences cannot be simultaneously obtained at present.
MACHINE READING METHOD, SYSTEM, DEVICE, AND STORAGE MEDIUM