Abstract: The present invention provides a joint optimization method and system for delay and spectrum occupation in a cloud-edge collaborative network. The method includes: initializing a cloud-edge collaborative network, and generating a set of user requests; establishing a target function of minimum average end-to-end delay and minimum spectrum slot occupation of a user request; during processing of each user request based on the target function, sequentially determining whether a node and path selection uniqueness constraint, a mobile edge computing (MEC) server load constraint, a spectrum resource occupation and uniqueness constraint, a spectrum continuity constraint, and a spectrum consistency constraint are satisfied, where if all constraints are satisfied, the user request is successfully processed, and the process turns to step S4; or if any constraint is not satisfied, the user request fails to be processed; and calculating average end-to-end delay and spectrum resource occupancy of the user request.

Abstract: A method includes generating a base model by training with a first dataset of data pairs and generating an adapted model by training the base model on a second dataset of data pairs. The method also includes determining a contrastive score for each data pair of a third dataset of data pairs using the base model and the adapted model. The contrastive score is indicative of a probability of quality of the respective data pair. The method also includes training a target model using the data pairs of the third dataset and the contrastive scores.

Abstract: A method includes generating a base model by training with a first dataset of data pairs and generating an adapted model by training the base model on a second dataset of data pairs. The method also includes determining a contrastive score for each data pair of a third dataset of data pairs using the base model and the adapted model. The contrastive score is indicative of a probability of quality of the respective data pair. The method also includes training a target model using the data pairs of the third dataset and the contrastive scores.

Abstract: Aspects of the technology employ a machine translation quality prediction (MTQP) model to refine datasets that are used in training machine translation systems. This includes receiving, by a machine translation quality prediction model, a sentence pair of a source sentence and a translated output (802). Then performing feature extraction on the sentence pair using a set of two or more feature extractors, where each feature extractor generates a corresponding feature vector (804). The corresponding feature vectors from the set of feature extractors are concatenated together (806). And the concatenated feature vectors are applied to a feedforward neural network, in which the feedforward neural network generates a machine translation quality prediction score for the translated output (808).

Abstract: A method includes generating a base model by training with a first dataset of data pairs and generating an adapted model by training the base model on a second dataset of data pairs. The method also includes determining a contrastive score for each data pair of a third dataset of data pairs using the base model and the adapted model. The contrastive score is indicative of a probability of quality of the respective data pair. The method also includes training a target model using the data pairs of the third dataset and the contrastive scores.