  Multi-label text classification (MLC) is a challenging task in settings of
large label sets, where label support follows a Zipfian distribution. In this
paper, we address this problem through retrieval augmentation, aiming to
improve the sample efficiency of classification models. Our approach closely
follows the standard MLC architecture of a Transformer-based encoder paired
with a set of classification heads. In our case, however, the input document
representation is augmented through cross-attention to similar documents
retrieved from the training set and represented in a task-specific manner. We
evaluate this approach on four datasets from the legal and biomedical domains,
all of which feature highly skewed label distributions. Our experiments show
that retrieval augmentation substantially improves model performance on the
long tail of infrequent labels especially so for lower-resource training
scenarios and more challenging long-document data scenarios.
