Deep Prompt Multi-task Network for Abuse Language Detection: A Novel Approach to Enhance Detection Accuracy

Core Concepts

The author proposes a novel Deep Prompt Multi-task Network (DPMN) to address the limitations of existing abuse language detection methods by utilizing prompt-based learning and multi-task learning.

Abstract

The content introduces the challenges in detecting abusive language on social networks and presents the proposed DPMN approach. It discusses the importance of prompt-based learning, multi-task network architecture, and experimental results showing superior performance compared to existing methods.
The article highlights the evolution of abuse language detection from conventional machine learning to deep learning methods and large pre-trained language models like BERT. It emphasizes the need for more effective utilization of PLMs' knowledge through prompt-based learning.
Key components of DPMN include deep prompt tuning, light prompt tuning, task head based on Bi-LSTM and FFN, and multi-task learning. The experiments conducted on three public datasets demonstrate that DPMN outperforms state-of-the-art methods in detecting abusive language.
The study also includes ablation experiments to analyze the contributions of different components in DPMN, showcasing the effectiveness of deep continuous prompt learning. Additionally, convergence analysis and implementation details are provided to support the experimental findings.

Stats

Macro F1 scores of DPMN: 0.8384 (OLID), 0.9218 (SOLID), 0.8165 (AbuseAnalyzer)

Quotes

"It is essential to minimize the psychological toll on victims to stop hate crimes."
"Prompt tuning has been a great success for most natural language processing tasks."
"The proposed DPMN achieves excellent results in detecting abusive language."

Key Insights Distilled From

Deep Prompt Multi-task Network for Abuse Language Detection

by Jian Zhu,Yup... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05268.pdf

Deeper Inquiries

How can prompt-based learning be further optimized for abuse language detection?

Prompt-based learning can be further optimized for abuse language detection by exploring different prompt lengths, forms, and initialization methods. Experimenting with various combinations of continuous prompt tokens and tuning strategies can help determine the most effective approach. Additionally, incorporating domain-specific knowledge into the prompts could enhance the model's understanding of abusive language nuances. Fine-tuning the prompt encoder module to generate more informative prompts tailored specifically for abuse language detection tasks can also improve performance.

What are potential drawbacks or limitations of using a multi-task network like DPMN?

While multi-task networks like DPMN offer advantages in leveraging shared representations and transferring knowledge across tasks, they come with certain drawbacks and limitations. One limitation is the complexity introduced by managing multiple tasks simultaneously, which may increase computational costs and training time. Balancing task weights in multi-task learning can be challenging, as assigning unequal importance to different tasks may impact overall performance. Additionally, overfitting on one task at the expense of others is a common risk in multi-task networks if not properly regularized or weighted.

How might advancements in large-scale PLMs impact future developments in abuse language detection?

Advancements in large-scale Pre-trained Language Models (PLMs) are likely to have a significant impact on future developments in abuse language detection. These models provide a strong foundation for understanding natural language nuances and context, enabling more accurate identification of abusive content online. By fine-tuning these advanced PLMs specifically for abuse language detection tasks through techniques like deep prompt tuning or continuous prompts, researchers can harness their vast linguistic knowledge to improve accuracy and efficiency in detecting abusive language patterns. As PLMs continue to evolve with larger datasets and improved architectures, they will play a crucial role in enhancing automated systems' ability to combat online harassment and hate speech effectively.