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Deep Attention Diffusion Graph Neural Networks for Text Classification
Text classification is a fundamental task with broad applications in natural language processing.
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks.
Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification
Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness.
Compositional Learning of Relation Path Embedding for Knowledge Base Completion
In this paper, we study the problem of how to better embed entities and relations of knowledge bases into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths, and we propose a compositional learning model of relation path embedding (RPE).
