Titlebook: Web Information Systems and Applications; 21st International C Cheqing Jin,Shiyu Yang,Yong Zhang Conference proceedings 2024 The Editor(s)

鉗子

Contrastive Learning-Based Cross-Domain Data Augmentation for?Aspect-Based Sentiment Analysisich is rich in labeled data, to the target domain which lacks labeled data. Many recent studies have attempted to address this issue by generating a large amount of labeled target domain data, and the domain adaptive model . has achieved state-of-the-art results. However, training this model require

勉勵

平庸的人或物

Mast-Cell

雪崩

惡心

Goblet-Cells

Antioxidant

Dual Learning Model of?Code Summary and?Generation Based on?Transformerilizing the probability correlation between CS and CG but also promote alignment between CS and CG models. Based on this, we propose a dual-learning algorithm for CS and CG. Experiments on real Java and Python datasets demonstrated that our model significantly improved the results of CS and CG tasks, surpassing the performance of existing models.

cacophony

歸功于

Relation-Oriented Temporal Knowledge Graphs Completion Based on Recurrent Neural Networkce of relational information and temporal information. Next, the output value of the cyclic neural network layer is recoded to obtain the final value of the relational representation vector. Finally, our model utilizes the negative sample sampling to predict entities, thus significantly improving the performance of the knowledge graphs completion.