Few-shot NLP research is highly active, yet conducted in disjoint research threads with
evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful …

We study the problem of few shot learning for named entity recognition. Specifically, we
leverage the semantic information in the names of the labels as a way of giving the model …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

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 …

Prompting method is regarded as one of the crucial progress for few-shot nature language
processing. Recent research on prompting moves from discrete tokens based``hard …

Supervised machine learning approaches often rely on a" ground truth" label. However,
obtaining one label through majority voting ignores the important subjectivity information in …

Zero-shot learning is an approach where models generalize to unseen tasks without direct
training on them. We introduce the Unified Multiple-Choice (UniMC) framework, which is …

Intent detection is at the core of task-oriented dialogue systems. Existing intent detection
systems are typically trained with a large amount of data over a predefined set of intent …

Recommending appropriate academic journal to researchers has become a time-
consuming and challenging task. In this paper, we propose a Journal Name Semantic …

Multi-label intent detection aims to assign multiple labels to utterances and attracts
increasing attention as a practical task in task-oriented dialogue systems. As dialogue …