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 …
K He, R Mao, T Gong, C Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning …
Y Wang, C Xu, Q Sun, H Hu, C Tao, X Geng… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …
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 …
ChatGPT frequently appears in the media, with many predicting significant disruptions, especially in the fields of accounting and auditing. Yet research has demonstrated relatively …
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 …