Neural sequence labeling is widely adopted for many Natural Language Processing (NLP)
tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and …

Linguistic sequence labeling is a general approach encompassing a variety of problems,
such as part-of-speech tagging and named entity recognition. Recent advances in neural …

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 …

We propose a generative framework for joint sequence labeling and sentence-level
classification. Our model performs multiple sequence labeling tasks at once using a single …

Recently, prompt-based methods have achieved significant performance in few-shot
learning scenarios by bridging the gap between language model pre-training and fine …

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 …

Pre-trained word embeddings learned from unlabeled text have become a standard
component of neural network architectures for NLP tasks. However, in most cases, the …

Prompt-based methods have been successfully applied in sentence-level few-shot learning
tasks, mostly owing to the sophisticated design of templates and label words. However …

Few-shot named entity recognition (NER) systems aim at recognizing novel-class named
entities based on only a few labeled examples. In this paper, we present a decomposed …

We propose a multi-lingual multi-task architecture to develop supervised models with a
minimal amount of labeled data for sequence labeling. In this new architecture, we combine …