TEDB System Description to a Shared Task on Euphemism Detection 2022

• URL: http://arxiv.org/abs/2301.06602v1
• Date: Mon, 16 Jan 2023 20:37:56 GMT
• Title: TEDB System Description to a Shared Task on Euphemism Detection 2022
• Authors: Peratham Wiriyathammabhum
• Abstract summary: We considered Transformer-based models which are the current state-of-the-art methods for text classification. Our best result of 0.816 F1-score consists of a euphemism-detection-finetuned/TimeLMs-pretrained RoBERTa model as a feature extractor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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