  Named entity recognition (NER) models often struggle with noisy inputs, such
as those with spelling mistakes or errors generated by Optical Character
Recognition processes, and learning a robust NER model is challenging. Existing
robust NER models utilize both noisy text and its corresponding gold text for
training, which is infeasible in many real-world applications in which gold
text is not available. In this paper, we consider a more realistic setting in
which only noisy text and its NER labels are available. We propose to retrieve
relevant text of the noisy text from a knowledge corpus and use it to enhance
the representation of the original noisy input. We design three retrieval
methods: sparse retrieval based on lexicon similarity, dense retrieval based on
semantic similarity, and self-retrieval based on task-specific text. After
retrieving relevant text, we concatenate the retrieved text with the original
noisy text and encode them with a transformer network, utilizing self-attention
to enhance the contextual token representations of the noisy text using the
retrieved text. We further employ a multi-view training framework that improves
robust NER without retrieving text during inference. Experiments show that our
retrieval-augmented model achieves significant improvements in various noisy
NER settings.
