The rise of deep learning methods has transformed the research area of natural language processing beyond recognition. New benchmark performances are reported on a daily basis ranging from machine translation to question-answering. Yet, some of the unsolved practical research questions are not in the spotlight and this includes, for example, issues arising at the interface between spoken and written language processing.
We identify sentence boundary detection and speaker change detection applied to automatically transcribed texts as two NLP problems that have not yet received much attention but are nevertheless of practical relevance. We frame both problems as binary tagging tasks that can be addressed by fine-tuning a transformer model and we report promising results.