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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.
Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering
For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question.
