  Punctuation is critical in understanding natural language text. Currently,
most automatic speech recognition (ASR) systems do not generate punctuation,
which affects the performance of downstream tasks, such as intent detection and
slot filling. This gives rise to the need for punctuation restoration. Recent
work in punctuation restoration heavily utilizes pre-trained language models
without considering data imbalance when predicting punctuation classes. In this
work, we address this problem by proposing a token-level supervised contrastive
learning method that aims at maximizing the distance of representation of
different punctuation marks in the embedding space. The result shows that
training with token-level supervised contrastive learning obtains up to 3.2%
absolute F1 improvement on the test set.
