  The typical way for relation extraction is fine-tuning large pre-trained
language models on task-specific datasets, then selecting the label with the
highest probability of the output distribution as the final prediction.
However, the usage of the Top-k prediction set for a given sample is commonly
overlooked. In this paper, we first reveal that the Top-k prediction set of a
given sample contains useful information for predicting the correct label. To
effectively utilizes the Top-k prediction set, we propose Label Graph Network
with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we
build a label graph to review candidate labels in the Top-k prediction set and
learn the connections between them. We also design a dynamic $k$-selection
mechanism to learn more powerful and discriminative relation representation.
Our experiments show that KLG achieves the best performances on three relation
extraction datasets. Moreover, we observe that KLG is more effective in dealing
with long-tailed classes.
