Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

Supervised machine learning has several drawbacks that make it difficult to use in many
situations. Drawbacks include heavy reliance on massive training data, limited …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

As an essential task in task-oriented dialog systems, slot filling requires extensive training
data in a certain domain. However, such data are not always available. Hence, cross …

Abstract Named Entity Recognition (NER) remains difficult in real-world settings; current
challenges include short texts (low context), emerging entities, and complex entities (eg …

Named entity recognition (NER) models generally perform poorly when large training
datasets are unavailable for low-resource domains. Recently, pre-training a large-scale …

Current work in named entity recognition (NER) shows that data augmentation techniques
can produce more robust models. However, most existing techniques focus on augmenting …

The objective of few-shot named entity recognition is to identify named entities with limited
labeled instances. Previous works have primarily focused on optimizing the traditional token …

Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-
world scenario. A common practice is first to learn a NER model in a rich-resource general …

Cross-domain NER is a challenging task to address the low-resource problem in practical
scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language …