Most NER methods rely on extensive labeled data for model training, which struggles in the
low-resource scenarios with limited training data. Existing dominant approaches usually …

Past progress on neural models has proven that named entity recognition is no longer a
problem if we have enough labeled data. However, collecting enough data and annotating …

In this work, we examine the ability of NER models to use contextual information when
predicting the type of an ambiguous entity. We introduce NRB, a new testbed carefully …

NER model has achieved promising performance on standard NER benchmarks. However,
recent studies show that previous approaches may over-rely on entity mention information …

To audit the robustness of named entity recognition (NER) models, we propose RockNER, a
simple yet effective method to create natural adversarial examples. Specifically, at the entity …

Natural language processing models often have to make predictions on text data that
evolves over time as a result of changes in language use or the information described in the …

Named entity recognition is a key component of many text processing pipelines and it is thus
essential for this component to be robust to different types of input. However, domain transfer …

In real dialogue scenarios, as there are unknown input noises in the utterances, existing
supervised slot filling models often perform poorly in practical applications. Even though …