Euphemism is an indirect way to express sensitive topics. People can comfortably communicate with each other about sensitive topics or taboos by using euphemisms. The Euphemism Detection Shared Task in the Third Workshop on Figurative Language Processing co-located with EMNLP 2022 provided a euphemism detection dataset that was divided into the train set and the test set. We made euphemism detection experiments by prompt tuning pre-trained language models on the dataset. We used RoBERTa as the pre-trained language model and created suitable templates and verbalizers for the euphemism detection task. Our approach achieved the third-best score in the euphemism detection shared task. This paper describes our model participating in the task.

The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.