  In this work, we unify several existing decoding strategies for punctuation
prediction in one framework and introduce a novel strategy which utilises
multiple predictions at each word across different windows. We show that
significant improvements can be achieved by optimising these strategies after
training a model, only leading to a potential increase in inference time, with
no requirement for retraining. We further use our decoding strategy framework
for the first comparison of tagging and classification approaches for
punctuation prediction in a real-time setting. Our results show that a
classification approach for punctuation prediction can be beneficial when
little or no right-side context is available.
