  Disentangling conversations mixed together in a single stream of messages is
a difficult task, made harder by the lack of large manually annotated datasets.
We created a new dataset of 77,563 messages manually annotated with
reply-structure graphs that both disentangle conversations and define internal
conversation structure. Our dataset is 16 times larger than all previously
released datasets combined, the first to include adjudication of annotation
disagreements, and the first to include context. We use our data to re-examine
prior work, in particular, finding that 80% of conversations in a widely used
dialogue corpus are either missing messages or contain extra messages. Our
manually-annotated data presents an opportunity to develop robust data-driven
methods for conversation disentanglement, which will help advance dialogue
research.
