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.

我们提出了一种用于滥用语言检测的全新的深度提示多任务网络 (DPMN)。DPMN 首先尝试设计了对预训练语言模型 (PLMs) 的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、调优策略和初始化方法对滥用语言检测的影响。此外，我们提出了一种基于 Bi-LSTM 和 FFN 的任务头，可用作短文本分类器。最终，DPMN 利用多任务学习进一步提高了检测性能。在 OLID、SOLID 和 AbuseAnalyzer 三个公开数据集上对提出的 DPMN 进行了评估，实验结果表明我们的 DPMN 优于现有方法。