The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content. Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and violence on social media platforms. The existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models, however, they considered only the analysis of abusive content features generated through annotated datasets. This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora in dealing with the imbalanced and scarcity of labeled datasets. Our analysis are using two well-known Transformer-based models, BERT and mBERT, where the later is used to address abusive content detection in multi-lingual scenarios. Our model jointly learns abusive content detection with emotional features by sharing representations through transformers' shared encoder. This approach increases data efficiency, reduce overfitting via shared representations, and ensure fast learning by leveraging auxiliary information. Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets. Our hate speech detection Multi-task model exhibited 3% performance improvement over baseline models, but the performance of multi-task models were not significant for offensive language detection task. More interestingly, in both tasks, multi-task models exhibits less false positive errors compared to single task scenario.

翻译：社交媒体平台的兴起从根本上改变了人们的交流方式，但与此同时，网络上攻击性内容的使用也随之增加。因此，自动检测这些内容对于禁止不当信息、减少社交媒体平台上的有害和暴力行为至关重要。现有基于预训练Transformer模型的仇恨言论与攻击性语言检测研究虽取得了显著成果，但仅局限于分析由标注数据集生成的攻击内容特征。本文提出一种多任务联合学习方法，通过整合从其他语料库中提取的外部情感特征，以应对标注数据集的不平衡性和稀缺性问题。我们采用两种著名的基于Transformer的模型——BERT和mBERT（后者用于处理多语言场景下的攻击内容检测），通过Transformer共享编码器实现攻击内容检测与情感特征的表示共享。该方法通过共享表征提高了数据效率，减少了过拟合，并借助辅助信息实现了快速学习。研究结果表明，情感知识有助于更可靠地跨数据集识别仇恨言论和攻击性语言。在仇恨言论检测任务中，本文多任务模型的性能较基线模型提升了3%，但在攻击性语言检测任务中未表现出显著优势。值得注意的是，在两个任务中，多任务模型相较于单任务场景均表现出更少的假阳性错误。