This paper highlights the significance of natural language processing (NLP) within artificial intelligence, underscoring its pivotal role in comprehending and modeling human language. Recent advancements in NLP, particularly in conversational bots, have garnered substantial attention and adoption among developers. This paper explores advanced methodologies for attaining smaller and more efficient NLP models. Specifically, we employ three key approaches: (1) training a Transformer-based neural network to detect offensive language, (2) employing data augmentation and knowledge distillation techniques to increase performance, and (3) incorporating multi-task learning with knowledge distillation and teacher annealing using diverse datasets to enhance efficiency. The culmination of these methods has yielded demonstrably improved outcomes.

本研究针对自然语言处理（NLP）在进攻性语言检测中的应用，提出了一种新颖的方法，通过融合知识蒸馏和多任务学习以提高模型效率。研究表明，结合数据增强和先进的技术手段，可以显著提升进攻性语言检测模型的性能，对相关领域具有重要影响。

通过知识蒸馏、多任务学习和数据增强提升罗马尼亚进攻性语言检测