  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.
