The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse language detection. Specifically, DPMN first attempts to design two forms of deep prompt tuning and light prompt tuning for the PLMs. The effects of different prompt lengths, tuning strategies, and prompt initialization methods on detecting abusive language are studied. In addition, we propose a Task Head based on Bi-LSTM and FFN, which can be used as a short text classifier. Eventually, DPMN utilizes multi-task learning to improve detection metrics further. The multi-task network has the function of transferring effective knowledge. The proposed DPMN is evaluated against eight typical methods on three public datasets: OLID, SOLID, and AbuseAnalyzer. The experimental results show that our DPMN outperforms the state-of-the-art methods.

翻译：辱骂语言检测随着社交网络的广泛使用仍是一个长期挑战，检测任务面临准确率有限的问题。现有检测方法通常采用预训练语言模型（PLMs）的微调技术处理下游任务，然而这些方法未能充分激发PLMs的通用知识。针对此问题，我们提出了一种新型深度提示多任务网络（DPMN）用于辱骂语言检测。具体而言，DPMN首次为PLMs设计了深度提示调优和轻提示调优两种形式，研究了不同提示长度、调优策略及提示初始化方法对辱骂语言检测效果的影响。此外，基于双向长短期记忆网络（Bi-LSTM）和前馈神经网络（FFN）提出了任务头（Task Head），可作为短文本分类器使用。最终，DPMN利用多任务学习进一步优化检测指标，该多任务网络具有有效知识迁移功能。在OLID、SOLID和AbuseAnalyzer三个公开数据集上，将所提DPMN与八种典型方法进行了对比评估。实验结果表明，DPMN性能优于现有最先进方法。