Abstract: A method for processing a query-response information is provided, which relates to a field of artificial intelligence technology, and in particular to fields of deep learning, large models, intelligent query and response, etc. The method for processing a query-response information includes: generating at least one initial response information according to a query information provided by an object; acquiring at least one feedback information corresponding to the at least one initial response information, wherein the feedback information indicates a preference degree of the object for the initial response information; and generating a training sample according to the query information, the at least one initial response information and the at least one feedback information. The present disclosure further provides a method for training a conversational model, an electronic device, and a storage medium.

Abstract: A method for generating a dialogue state includes: acquiring a target dialogue state of a previous round of dialogue and dialogue information of a current round of dialogue; generating an initial dialogue state of the current round of dialogue according to the target dialogue state of the previous round of dialogue and the dialogue information of the current round of dialogue; and generating a target dialogue state of the current round of dialogue according to the initial dialogue state of the current round of dialogue and the dialogue information of the current round of dialogue.

Abstract: A method for dialogue processing, an electronic device and a storage medium are provided. The specific technical solution includes: obtaining a dialogue history; selecting a target machine from a plurality of machines; inputting the dialogue history into a trained dialogue model in the target machine to generate a response to the dialogue history, in which the dialogue model comprises a common parameter and a specific parameter, and different machines correspond to the same common parameter.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; 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.

Abstract: A method for generating a dialogue state includes: acquiring a target dialogue state of a previous round of dialogue and dialogue information of a current round of dialogue; generating an initial dialogue state of the current round of dialogue according to the target dialogue state of the previous round of dialogue and the dialogue information of the current round of dialogue; and generating a target dialogue state of the current round of dialogue according to the initial dialogue state of the current round of dialogue and the dialogue information of the current round of dialogue.

Abstract: A method for dialogue processing, an electronic device and a storage medium are provided. The specific technical solution includes: obtaining a dialogue history; selecting a target machine from a plurality of machines; inputting the dialogue history into a trained dialogue model in the target machine to generate a response to the dialogue history, in which the dialogue model comprises a common parameter and a specific parameter, and different machines correspond to the same common parameter.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; 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.