Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)
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| Challenge: | Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity. |
| Approach: | They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families. |
| Outcome: | The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets. |
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| Challenge: | Existing active learning models for text spam detection tasks are based on pool-based active learning, but the annotating process is laborious and time consuming for humans. |
| Approach: | They propose a semi-supervised active learning model to address spam imbalances . they propose masked attention learning approach and character variation graph-enhanced augmentation procedure . |
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Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
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VCWE: Visual Character-Enhanced Word Embeddings (N19-1)
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| Challenge: | Currently, word embeddings are playing a pivotal role in many natural language processing tasks. |
| Approach: | They propose a model to learn Chinese word embeddings via three-level composition . they use convolutional neural network to extract intra-character compositionality from character shape . |
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Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation (2020.aacl-srw)
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| Challenge: | a character-based text classification framework for non-alphabetic languages is proposed . glyphs are used to give visual meaning to characters, such as "" and "." |
| Approach: | They propose a character-based text classification framework for non-alphabetic languages . they propose glyph-aware disentangled character embeddings that are dimensionally independent . |
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GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model (D18-1)
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| Challenge: | Conventional topic models suffer different problems in different settings. |
| Approach: | They propose a novel way to model word-pairs named biterms in the whole corpus and a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterm. |
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C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts (2026.findings-acl)
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| Challenge: | Recent efforts to develop algorithms for large language models (LLMs) have limited model diversity and data homogeneity in the Chinese corpora. |
| Approach: | They propose a Chinese Real-prompt AI-generated text Detection benchmark that can be generalized to unseen LLMs and external Chinese datasets. |
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Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings (2025.findings-acl)
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Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu
| Challenge: | Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs. |
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Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
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E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
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Multiple Character Embeddings for Chinese Word Segmentation (P19-2)
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| Challenge: | Chinese word segmentation is regarded as character-based sequence labeling task in most current work but it neglects important fact: Chinese characters contain both semantic and phonetic meanings. |
| Approach: | They propose a shared bi-LSTM-CRF model which fuses linguistic features efficiently by sharing the LSTM network during the training procedure. |
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