Abstract
Patronizing and Condescending Language (PCL) is a form of implicitly toxic speech aimed at vulnerable groups with the potential to cause them long-term harm. As an emerging field of toxicity detection, it still lacks high-quality annotated corpora (especially in the Chinese field). Existing PCL datasets lack fine-grained annotation of toxicity level, resulting in a loss of edge information. In this paper, we make the first attempt at fine-grained condescending detection in Chinese. First, we propose CondescendCN Frame, a hierarchical framework for fine-grained condescending detection. On this basis, we introduce CCPC, a hierarchical Chinese corpus for PCL, with 11k structured annotations of social media comments from Sina Weibo and Zhihu. We find that adding toxicity strength (TS) can effectively improve the detection ability of PCL and demonstrate that the trained model still retains decent detection capabilities after being migrated to a larger variety of media data (over 120k).Due to the subjective ambiguity of PCL, more contextual information and subject knowledge expansion are critically required for this field.
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Notes
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Our dataset and code are available on https://github.com/dut-laowang/CCPC.
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Wang, H., Li, M., Lu, J., Yang, L., Xia, H., Lin, H. (2023). CCPC: A Hierarchical Chinese Corpus for Patronizing and Condescending Language Detection. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14303. Springer, Cham. https://doi.org/10.1007/978-3-031-44696-2_50
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