TEDB System Description to a Shared Task on Euphemism Detection 2022
Peratham Wiriyathammabhum · 2022
In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022.We cast the task of predicting euphemism as text classification.We considered Transformer-based models which are the current state-of-the-art methods for text classification.We explored different training schemes, pretrained models, and model architectures.Our best result of 0.816 F1-score (0.818 precision and 0.814 recall) consists of a euphemism-detection-finetuned TweetEval/TimeLMs-pretrained RoBERTa model as a feature extractor frontend with a KimCNN classifier backend trained end-to-end using a cosine annealing scheduler.We observed pretrained models on sentiment analysis and offensiveness detection to correlate with more F1-score while pretraining on other tasks, such as sarcasm detection, produces less F1-scores.Also, putting more word vector channels does not improve the performance in our experiments.1 https://github.com/perathambkk/euphemism_ 1