基于RoBERTa-MTL融合语言特征的有害文本识别OA

[Purpose]To address the limitations of traditional text recognition models in handling the diverse and subtle nature of toxic con-tent on social media,we aim to develop more precise and efficient detection methods.This will improve the accuracy and generalizability of toxic content identification,thereby fostering a healthier and safer online environment.[Method]This study proposes a method based on RoBERTa and multi-task joint learning,which utilizes RoBERTa to extract text embeddings,constructs a shared encoder and multiple task-specific encoders to capture general and task-specific features respectively,and integrates these two types of features to generate the final representation of the text.[Result/Conclusion]The experimental results demonstrate that the multi-task model improves accuracy,precision,and recall by approximately 10%compared to traditional text classification methods.Furthermore,in contrast to traditional sin-gle-task toxic content detection methods,the multi-task model can leverage the relationships between different types of toxic content,thereby improving the overall performance of toxic content detection.