Patent ID: 11947920
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 14:
15. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements operations comprising:
obtaining a current dialogue sentence input by a user;
using the current dialogue sentence and a goal type and a goal entity obtained before the current dialogue sentence as an input of a first neural network module of a neural network system, and generating the goal type and the goal entity of the current dialogue sentence by performing feature extraction through the first neural network module; and
using the current dialogue sentence, the goal type and the goal entity of the current dialogue sentence and knowledge base data as an input of a second neural network module of the neural network system, and generating a reply sentence by performing feature extraction and classification through the second neural network module,
wherein using the current dialogue sentence, the goal type and the goal entity of the current dialogue sentence and knowledge base data as an input of a second neural network module of the neural network system, and generating a reply sentence by performing feature extraction and classification through the second neural network module, comprises:
the current dialogue sentence being input to a natural language processing model of the second neural network module, and encoded by the natural language processing model to extract semantics, so as to generate a second vector;
the goal type and the goal entity of the current dialogue sentence being input to a first Bi-directional Gated Recurrent Unit model of the second neural network module, and encoded by the first Bi-directional Gated Recurrent Unit model, so as to generate a third vector
combining the second vector and the third vector to obtain a fourth vector;
the knowledge base data being input to a second Bi-directional Gated Recurrent Unit model of the second neural network module, and encoded by the second Bi-directional Gated Recurrent Unit model, so as to generate a fifth vector;
performing an attention mechanism operation on the fourth vector and the fifth vector to obtain a sixth vector;
combining the fifth vector and the sixth vector to obtain a seventh vector; and the seventh vector being input to a single-layer neural network model of the second neural network module, and classified by the single-layer neural network model, so as to generate the reply sentence.