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This paper proposes a dual-threshold hard example mining strategy for cross-platform Chinese offensive comment detection, addressing performance degradation due to domain shift. The method fine-tunes a RoBERTa model on the COLD dataset and adapts it to four Chinese social media platforms with minimal labeled data.
This paper establishes a reproducible multi-architecture baseline for token-level Chinese metaphor identification using the MIPVU framework and the PSU Chinese Metaphor Corpus. It compares encoder models like RoBERTa and MelBERT against the Qwen3.5-9B generative model, releasing code and data to facilitate future research.
Researchers from Peking University introduce CFMS, the first fine-grained Chinese multimodal sarcasm detection benchmark with 2,796 image-text pairs and a triple-level annotation framework (sarcasm identification, target recognition, explanation generation), along with a novel RL-augmented in-context learning method (PGDS) that significantly outperforms existing baselines.