In few-shot text classification, self-training is a popular tool in semi-supervised learning (SSL). It relies on pseudo-labels to expand data, which has demonstrated success. However, these pseudo-labels contain potential noise and provoke a risk of underfitting the decision boundary. While the pseudo-labeled data can indeed be noisy, fully acquiring this flawed data can result in the accumulation of further noise and eventually impacting the model performance. Consequently, self-training presents a challenge: mitigating the accumulation of noise in the pseudo-labels. Confronting this challenge, we introduce superficial learning, inspired by pedagogy’s focus on essential knowledge. Superficial learning in pedagogy is a learning scheme that only learns the material ‘at some extent’, not fully understanding the material. This approach is usually avoided in education but counter-intuitively in our context, we employ superficial learning to acquire only the necessary context from noisy data, effectively avoiding the noise. This concept serves as the foundation for SuperST, our self-training framework. SuperST applies superficial learning to the noisy data and fine-tuning to the less noisy data, creating an efficient learning cycle that prevents overfitting to the noise and spans the decision boundary effectively. Notably, SuperST improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. We substantiate our claim through empirical experiments and decision boundary analysis.

Abusive text is a serious problem in social media and causes many issues among users as the number of users and the content volume increase. There are several attempts for detecting or preventing abusive text effectively. One simple yet effective approach is to use an abusive lexicon and determine the existence of an abusive word in text. This approach works well even when an abusive word is obfuscated. On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words. Especially, it is hard to identify sarcasm or offensiveness in context without any abusive words. We tackle this problem using an ensemble deep learning model. Our model consists of two parts of extracting local features and global features, which are crucial for identifying implicit abusiveness in context level. We evaluate our model using three benchmark data. Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words. Furthermore, we combine our model and an abusive lexicon method. The experimental results show that our model has at least 4% better performance compared with the previous approaches for identifying text abusiveness in case of with/without abusive words.