  Automatic speech recognition (ASR) is widely used in consumer electronics.
ASR greatly improves the utility and accessibility of technology, but usually
the output is only word sequences without punctuation. This can result in
ambiguity in inferring user-intent. We first present a transformer-based
approach for punctuation prediction that achieves 8% improvement on the IWSLT
2012 TED Task, beating the previous state of the art [1]. We next describe our
multimodal model that learns from both text and audio, which achieves 8%
improvement over the text-only algorithm on an internal dataset for which we
have both the audio and transcriptions. Finally, we present an approach to
learning a model using contextual dropout that allows us to handle variable
amounts of future context at test time.
