  Long-tailed relation classification is a challenging problem as the head
classes may dominate the training phase, thereby leading to the deterioration
of the tail performance. Existing solutions usually address this issue via
class-balancing strategies, e.g., data re-sampling and loss re-weighting, but
all these methods adhere to the schema of entangling learning of the
representation and classifier. In this study, we conduct an in-depth empirical
investigation into the long-tailed problem and found that pre-trained models
with instance-balanced sampling already capture the well-learned
representations for all classes; moreover, it is possible to achieve better
long-tailed classification ability at low cost by only adjusting the
classifier. Inspired by this observation, we propose a robust classifier with
attentive relation routing, which assigns soft weights by automatically
aggregating the relations. Extensive experiments on two datasets demonstrate
the effectiveness of our proposed approach. Code and datasets are available in
https://github.com/zjunlp/deepke.
