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：随着社交网络的广泛使用，辱骂语言的检测仍是一个长期存在的挑战。辱骂语言的检测任务面临准确率有限的问题。我们认为，现有的检测方法利用预训练语言模型（PLMs）的微调技术来处理下游任务，因此未能充分激发PLMs的通用知识。为解决此问题，我们提出了一种新颖的深度提示多任务网络（DPMN）用于辱骂语言检测。具体而言，DPMN首先尝试为PLMs设计两种形式的提示调优：深度提示调优与轻量提示调优。我们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的影响。此外，我们提出一个基于Bi-LSTM和FFN的任务头，可作为短文本分类器使用。最终，DPMN利用多任务学习进一步改进检测指标。该多任务网络具备迁移有效知识的功能。我们在三个公开数据集（OLID、SOLID和AbuseAnalyzer）上，将所提出的DPMN与八种典型方法进行了对比评估。实验结果表明，我们的DPMN性能优于现有最先进方法。