TL;DR
A novel HyperGraph attention LSTM network (LSTM-HyG) is proposed to capture high-level semantics for sentences, and negated verbs, negative cues, by a fine-grained label scheme that can learn adequate sequential dependency relationship of words.
The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency, which makes the framework suitable for practical hate speech moderation systems that require accurate, generalizable, and explainable decisions.
This study proposes a hybrid deep-learning framework that combines convolutional feature extraction with a bidirectional recurrent encoder and an attention mechanism to categories tweets as positive, neutral, or negative, and indicates that the learned representations generalize reasonably well across domains rather th...
QMPN, Quality-Aware Memory Prompting Network, is proposed, that stores sample-specific prompts derived from a small support set, retrieves relevant prompting evidence for each query, and uses the retrieved prompts to guide aspect-aware context generation.
Findings provide evidence that truthfulness is a structured, linearly separable concept in the latent space of pretrained language models, and point toward interpretability-driven misinformation detection as a practical complement to retrieval-based pipelines.
We present our system for Task B of the Cross-Cultural Misogynistic Meme Detection Challenge, addressing misogyny detection across Tamil, Malayalam, Chinese, and English memes. Meme comprehension inherently depends on the complex interplay between textual and visual cues, further complicated by culturally specific cont...
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