  Punctuation restoration is an important task in automatic speech recognition
(ASR) which aim to restore the syntactic structure of generated ASR texts to
improve readability. While punctuated texts are abundant from written
documents, the discrepancy between written punctuated texts and ASR texts
limits the usability of written texts in training punctuation restoration
systems for ASR texts. This paper proposes a reinforcement learning method to
exploit in-topic written texts and recent advances in large pre-trained
generative language models to bridge this gap. The experiments show that our
method achieves state-of-the-art performance on the ASR test set on two
benchmark datasets for punctuation restoration.
