Method and apparatus for generating dialogue model

An apparatus for generating a dialogue model, the apparatus comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, causing the at least one processor to perform operations, the operations comprising:
• acquiring a corpus sample set, a corpus sample comprising input information and target response information;
• classifying corpus samples in the corpus sample set, set discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample comprising the input information, the target response information, and a discrete hidden variable, wherein the corpus samples in the corpus sample set are classified according to a response direction of the target response information in the corpus sample set, and each value of the discrete hidden variables corresponds to a given response direction of the target response information;
• and
• training a preset neural network using the training sample set to obtain the dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.

A non-transitory computer readable medium, storing a computer program thereon, the program, when executed by a processor, causes the processor to perform operations, the operations comprising:

• acquiring a corpus sample set, a corpus sample comprising input information and target response information;
• classifying corpus samples in the corpus sample set, set discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample comprising the input information, the target response information, and a discrete hidden variable, wherein the corpus samples in the corpus sample set are classified according to a response direction of the target response information in the corpus sample set, and each value of the discrete hidden variables corresponds to a given response direction of the target response information;
• and
• training a preset neural network using the training sample set to obtain a dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.