Tencent Technology (shenzhen) Company Limited

Cheng Niu

Jie Zhou

Yan Zhang

Jian Hua Liu

Yan Wei Zhang

Jie Cao

Qiuge Liu

Shuo Sun

Yizhang Tan

Le Tian

Randy Zhong

Jin Zhang

Jinchao Zhang

Xiao Zhou

Qiu Ran

Fan Dong Meng

Zeyang Lei

Zekang Li

Yao Qiu

This disclosure relates to a method and an apparatus for training a text classification model. The method may include determining a semantic representation of the training sample using the text classification model and determining a predicted classification result of the training sample based on the semantic representation. The method may further include generating an adversarial sample corresponding to the training sample based on the training sample and perturbation information and determining a semantic representation of the adversarial sample corresponding to the training sample using the text classification model. The method may further include determining a classification loss of the text classification model based on the predicted classification result, determining a contrastive loss of the text classification model based on the semantic representation and the semantic representation of the adversarial sample, and training the text classification model based on the classification loss and the contrastive loss.

Method and apparatus for training text classification model

An encoding method is provided for an encoding apparatus. The method includes obtaining a target paragraph and a preset database, and inputting the target paragraph and preset database into a memory encoding model, obtaining, in an input layer, an original vector set of the target paragraph and a knowledge vector set of the preset database, obtaining, in a first memory layer, a first target sentence matrix according to the original vector set and the knowledge vector set, and obtaining, in an output layer, a paragraph vector of the target paragraph according to the first target sentence matrix, and performing processing based on the paragraph vector.

Encoding method, apparatus, and device, and storage medium

Embodiments of this disclosure include an encoding method and apparatus. The encoding may include obtaining a target paragraph and a context sentence of the target paragraph, and inputting the target paragraph and the context sentence into a memory encoding model. The encoding may further include obtaining an original vector set and a memory vector set in the input layer and obtaining a first target sentence matrix of the original vector set in the memory layer according to the original vector set and the memory vector set. The encoding may further include obtaining a paragraph vector of the target paragraph in the output layer according to the first target sentence matrix and performing processing based on the paragraph vector.

Encoding method, apparatus, and storage medium

The disclosure discloses a method and an apparatus for training a dialog generation model, and a dialog generation method and apparatus, and relates to the field of artificial intelligence. The method includes: encoding a context sample to obtain a first latent variable, and recognizing the first latent variable to obtain a prior latent variable; encoding a response sample to obtain a second latent variable; encoding a response similar sample to obtain a third latent variable; performing recognition according to a Gaussian mixture distribution of the first latent variable, the second latent variable, and the third latent variable to obtain a posterior latent variable; and matching the prior latent variable with the posterior latent variable, and performing adversarial training on a dialog generation model.

Method and apparatus for training dialog generation model, dialog generation method and apparatus, and medium

A method is performed at a backend server. The server receives a to-be-processed interaction message from a user interface of a social network platform. The server performs intention classification on the to-be-processed interaction message, to obtain an intention classification result of the to-be-processed interaction message. The server performs semantic analysis on the to-be-processed interaction message when the intention classification result comprises a mini program intention, to determine a message keyword of the to-be-processed interaction message. The server determines a program identifier of a target mini program corresponding to the to-be-processed interaction message according to the message keyword. The server transmits a message processing result based on the program identifier of the target mini program.

Interaction message processing method and apparatus, computer device, and storage medium

A man-machine conversation method includes: extracting a keyword from input information of a user; determining a candidate article according to the keyword; obtaining a plurality of sentences in the candidate article, and determining a score of each of the plurality of sentences; extracting part of the plurality of sentences based on the score of each of the plurality of sentences; and generating return information that includes the extracted part of the plurality of sentences.

Man-machine conversation method, electronic device, and computer-readable medium

A method is performed at an electronic device. The electronic device receives, in a user interface of a social network platform, a to-be-processed interaction message/ The device transmits a message processing request based on the to-be-processed interaction message to a remote server, wherein the remote server identifies one or more target mini application programs based on semantic analysis of the to-be-processed interaction message. The device receives, from the server, a message processing result in accordance with the message processing request. The message processing result includes one or more links to the target mini application programs running on the social network platform. The electronic device displays, in the user interface, a target interaction message for each target mini application program according to the message processing result.

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Shenzhen, China

Cheng Niu