# CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection

arXiv: https://arxiv.org/abs/2509.18562 · HTML: https://mlsanity.com/paper/2509.18562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding](https://mlsanity.com/paper/2502.06020/) (2025)
- [TalkDown: A Corpus for Condescension Detection in Context](https://mlsanity.com/paper/1909.11272/) (2019)
- [Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities](https://mlsanity.com/paper/2011.08320/) (2020)
- [AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment](https://mlsanity.com/paper/2412.00833/) (2024)
- [AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder](https://mlsanity.com/paper/2309.08738/) (2023)
- [The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement](https://mlsanity.com/paper/2408.00534/) (2024)

## Abstract (arXiv)

Chinese Patronizing and Condescending Language (CPCL) is an implicitly discriminatory toxic speech targeting vulnerable groups on Chinese video platforms. The existing dataset lacks user comments, which are a direct reflection of video content. This undermines the model's understanding of video content and results in the failure to detect some CPLC videos. To make up for this loss, this research reconstructs a new dataset PCLMMPLUS that includes 103k comment entries and expands the dataset size. We also propose the CPCLDetector model with alignment selection and knowledge-enhanced comment content modules. Extensive experiments show the proposed CPCLDetector outperforms the SOTA on PCLMM and achieves higher performance on PCLMMPLUS . CPLC videos are detected more accurately, supporting content governance and protecting vulnerable groups. Code and dataset are available at https://github.com/jiaxunyang256/PCLD.