  Class imbalance is a common challenge in many NLP tasks, and has clear
connections to bias, in that bias in training data often leads to higher
accuracy for majority groups at the expense of minority groups. However there
has traditionally been a disconnect between research on class-imbalanced
learning and mitigating bias, and only recently have the two been looked at
through a common lens. In this work we evaluate long-tail learning methods for
tweet sentiment and occupation classification, and extend a margin-loss based
approach with methods to enforce fairness. We empirically show through
controlled experiments that the proposed approaches help mitigate both class
imbalance and demographic biases.
