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CMNIE is a new information extraction benchmark for Chinese military news that jointly annotates events, entities, and relations to evaluate models on schema adherence and exact span matching.
GUIDE is a generative unsupervised framework for Chinese query correction that uses phonetic and visual shared-ID encoding to constrain corrections and adapt to changing vocabularies, outperforming baselines in experiments and online A/B testing.
This paper proposes SeTox, a search-augmented LLM framework for detecting implicit toxicity in Chinese neologisms by leveraging real-time web context and public consensus. Experiments show that even 3B-scale models outperform larger recent models on this task.
This paper proposes CNM, a lightweight augmentation that injects discrete compositional structure of Chinese characters into BERT via Ideographic Description Sequences, improving performance on rare and out-of-vocabulary characters while preserving general NLU accuracy.
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.