Abstract:
A computer-implemented method is provided for model training performed by a processing system. The method comprises determining a set of first weights based on a first matrix associated with a source model, determining a set of second weights based on the set of first weights, forming a second matrix associated with a target model based on the set of first weights and the set of second weights, initializing the target model based on the second matrix, and training the target model.
Abstract:
A syntax analysis method and apparatus are disclosed. The method includes: obtaining a source language sentence that is a translation of a target language sentence (S110); determining instances of state transition for the target language sentence according to the source language sentence and a correspondence between words of the target language sentence and words of the source language sentence (S120); and generating a syntax tree of the target language sentence according to the instances of state transition for the target language sentence (S130). The syntax analysis method and apparatus can improve efficiency of syntax analysis.
Abstract:
A word segmentation method and system for a language text, where in the method, a word segmentation is performed on the first language text in a first word segmentation manner to obtain a first word boundary set, the first word boundary set is divided into a trusted second word boundary set and an untrusted third word boundary set according to a confidence level threshold, a second language text is selected from the first language text according to the third word boundary set, and a word segmentation is performed on the second language text in a second word segmentation manner to obtain a fourth word boundary set. Word segmentation precision of the first language text can be flexibly adjusted by adjusting the confidence level threshold.
Abstract:
A message presenting method, device, and system relating to the field of computer technologies is provided, so as to resolve a problem in the prior art that an instant message presenting manner is relatively monotonous. The method includes: in embodiments of the present invention, classifying, by a server with regard to a message cluster, M number of messages entered in the message cluster by at least one user, under N number of classification topics, and sending the N number of classification topics and a target message to a client. In the technical solution, with regard to a message cluster, M number of messages entered in the message cluster by at least one user can be classified under N number of classification topics according to a preset classification rule, and the N number of classification topics and a target message can be sent to a client, so that the client can present the N number of classification topics to a user.
Abstract:
The present application discloses a service handover method, a network device and user equipment. In the service handover method provided in the present application, a device on a network side determines to hand over at least one service of the services of user equipment to a first target device and hand over the other service or services to a second target device, which not only can ensure offloading of the services by using the first target device, but also can make service quality of the services of the user equipment not be lowered because the first target device cannot fully meet a service quality requirement, thereby improving user experience.
Abstract:
The present application discloses a resource determining method and apparatus, which relates to the communications field and can reduce co-channel interference between systems of different standards that share a wireless spectrum resource. The method includes: acquiring relationship information of interference imposed on a first cell by a second cell, where the relationship information of interference is used to indicate interference imposed on the first cell by the second cell in a time-frequency domain, and the second cell and the first cell have different standards; and determining time-frequency resource information of the second cell according to the relationship information of interference, where the time-frequency resource information of the second cell is used for the second cell to perform time-frequency resource allocation.
Abstract:
A text processing model training method, and a text processing method and apparatus in the natural language processing field in the artificial intelligence field are disclosed. The training method includes: obtaining training text; separately inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model; the sample data includes a sample semantic feature and a sample label; the prediction data includes a prediction semantic feature and a prediction label; and the teacher model is a pre-trained language model used for text classification; and training a model parameter of the student model based on the sample data and the prediction data, to obtain a target student model. The method enables the student model to effectively perform knowledge transfer, thereby improving accuracy of a text processing result of the student model.
Abstract:
A text processing method, a model training method, and an apparatus related to the field of artificial intelligence is provided. The method includes: obtaining target knowledge data; processing the target knowledge data to obtain a target knowledge vector; processing to-be-processed text to obtain a target text vector; fusing the target text vector and the target knowledge vector based on a target fusion model, to obtain a fused target text vector and a fused target knowledge vector; and processing the fused target text vector and/or the fused target knowledge vector based on a target processing model, to obtain a processing result corresponding to a target task. The foregoing technical solution can improve accuracy of a result of processing a target task by the target processing model.
Abstract:
A method for starting a serverless container includes loading a code segment file of an application to create an application instance; and loading a snapshot file of the application, where the snapshot file includes a deserialized data segment of the application.
Abstract:
A method includes obtaining n pairs of translation sentences of a source language and a target language, where each of the n pairs of translation sentences includes a source language sentence and a target language sentence that are translations of each other, extracting a source language segment from each source language sentence in the n pairs of translation sentences using an extraction rule of the source language, extracting a target language segment from each target language sentence in the n pairs of translation sentences, and generating an extraction rule of the target language based on n target language segments extracted from n target language sentences.