Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

Few-shot text classification has recently been promoted by the meta-learning paradigm
which aims to identify target classes with knowledge transferred from source classes with …

Hate speech in social media is a growing phenomenon, and detecting such toxic content
has recently gained significant traction in the research community. Existing studies have …

Prompt tuning for pre-trained masked language models (MLM) has shown promising
performance in natural language processing tasks with few labeled examples. It tunes a …

With emerging topics (eg, COVID-19) on social media as a source for the spreading
misinformation, overcoming the distributional shifts between the original training domain (ie …

The wide spread of false information has detrimental effects on society, and false information
detection has received wide attention. When new domains appear, the relevant labeled data …

User intents are ever-changing, which requires deep learning models to have the ability to
classify unknown intents. Meta-learning aims to solve this problem by improving the model's …

Distribution estimation has been demonstrated as one of the most effective approaches in
dealing with few-shot image classification, as the low-level patterns and underlying …

Human brains integrate linguistic and perceptual information simultaneously to understand
natural language, and hold the critical ability to render imaginations. Such abilities enable …

Few-shot intent detection is a more challenging application. However, traditional prototypical
networks based on averaging often suffer from issues such as missing key information, poor …