Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

Medicare

Enhancing Continual Relation Extraction with Concept Aware Dynamic Memory Optimizationing works often rely on storing and replaying a fixed set of typical samples to prevent catastrophic forgetting. However, repeatedly replaying these samples may cause the biased latent features problem. In this paper, we find that the representations of memory samples will gradually lose representat

Acquired

modifier

Knowledge-Enhanced Context Representation for Unbiased Scene Graph Generationhips within a given image and to generate a structured representation of the scene. In order to enhance the model’s cognitive understanding of knowledge associations, this paper proposes a Knowledge-Enhanced Context Representation for Unbiased Scene Graph Generation model. To enhance the model, two

和平主义者

Knowledge-Enhanced Context Representation for Unbiased Scene Graph Generationhips within a given image and to generate a structured representation of the scene. In order to enhance the model’s cognitive understanding of knowledge associations, this paper proposes a Knowledge-Enhanced Context Representation for Unbiased Scene Graph Generation model. To enhance the model, two

好忠告人

Influx

Enhancing NER with Sentence-Level Entity Detection as an Simple Auxiliary Task model performance but also represents good generalization over multiple NER datasets. Our experiments on the MSRA and Weibo NER datasets show that our method could effectively boost the existing state-of-the-art NER methods, offering a compelling avenue for the advancement of efficient and robust NER methods.

不易燃

External Knowledge Enhancing Meta-learning Framework for Few-Shot Text Classification via Contrastivamples and their class prototypes. Furthermore, this paper employs an adversarial network to enhance the model’s generalization performance. The experiments show that the SCLAWM model has achieved remarkable performance on four benchmark datasets.

dominant

Enhancing NER with Sentence-Level Entity Detection as an Simple Auxiliary Task model performance but also represents good generalization over multiple NER datasets. Our experiments on the MSRA and Weibo NER datasets show that our method could effectively boost the existing state-of-the-art NER methods, offering a compelling avenue for the advancement of efficient and robust NER methods.

裂隙

External Knowledge Enhancing Meta-learning Framework for Few-Shot Text Classification via Contrastivamples and their class prototypes. Furthermore, this paper employs an adversarial network to enhance the model’s generalization performance. The experiments show that the SCLAWM model has achieved remarkable performance on four benchmark datasets.

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