  Capitalization and punctuation are important cues for comprehending written
texts and conversational transcripts. Yet, many ASR systems do not produce
punctuated and case-formatted speech transcripts. We propose to use a
multi-task system that can exploit the relations between casing and punctuation
to improve their prediction performance. Whereas text data for predicting
punctuation and truecasing is seemingly abundant, we argue that written text
resources are inadequate as training data for conversational models. We
quantify the mismatch between written and conversational text domains by
comparing the joint distributions of punctuation and word cases, and by testing
our model cross-domain. Further, we show that by training the model in the
written text domain and then transfer learning to conversations, we can achieve
reasonable performance with less data.
