  Existing conversational datasets consist either of written proxies for dialog
or small-scale transcriptions of natural speech. We introduce 'Interview': a
large-scale (105K conversations) media dialog dataset collected from news
interview transcripts. Compared to existing large-scale proxies for
conversational data, language models trained on our dataset exhibit better
zero-shot out-of-domain performance on existing spoken dialog datasets,
demonstrating its usefulness in modeling real-world conversations. 'Interview'
contains speaker role annotations for each turn, facilitating the development
of engaging, responsive dialog systems. In fact, experiments on two dialog
tasks show that leveraging such labels improves performance over strong
speaker-agnostic baselines, and enabling models to generate more specific and
inquisitive responses in interview-style conversations.
