Data annotation is a time-consuming and labor-intensive process for many NLP tasks.
Although there exist various methods to produce pseudo data labels, they are often task …

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low
resource domains. Existing approaches only learn class-specific semantic features and …

Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions,
and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning …

This paper focuses on the Data Augmentation for low-resource Natural Language
Understanding (NLU) tasks. We propose Prompt-based D} ata Augmentation model …

State-of-the-art deep neural networks require large-scale labeled training data that is often
expensive to obtain or not available for many tasks. Weak supervision in the form of domain …

ChatGPT frequently appears in the media, with many predicting significant disruptions,
especially in the fields of accounting and auditing. Yet research has demonstrated relatively …

Named entity recognition in real-world applications suffers from the diversity of entity types,
the emergence of new entity types, and the lack of high-quality annotations. To address the …

Training deep neural networks (DNNs) with limited supervision has been a popular research
topic as it can significantly alleviate the annotation burden. Self-training has been …

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, eg,
named entity recognition and slot filling, to generalize on an emerging, resource-scarce …

Fake news travels at unprecedented speeds, reaches global audiences and puts users and
communities at great risk via social media platforms. Deep learning based models show …