Publication
INTERSPEECH 2021
Conference paper

Token-level supervised contrastive learning for punctuation restoration

View publication

Abstract

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.

Date

30 Aug 2021

Publication

INTERSPEECH 2021

Authors

Share