Dramatic Conversation Disentanglement
Kent K. Chang Danica Chen David Bamman
We present a new dataset for studying conversation disentanglement in movies
and TV series. While previous work has focused on conversation disentanglement
in IRC chatroom dialogues, movies and TV shows provide a space for studying
complex pragmatic patterns of floor and topic change in face-to-face
multi-party interactions. In this work, we draw on theoretical research in
sociolinguistics, sociology, and film studies to operationalize a
conversational thread (including the notion of a floor change) in dramatic
texts, and use that definition to annotate a dataset of 10,033 dialogue turns
(comprising 2,209 threads) from 831 movies. We compare the performance of
several disentanglement models on this dramatic dataset, and apply the
best-performing model to disentangle 808 movies. We see that, contrary to
expectation, average thread lengths do not decrease significantly over the past
40 years, and characters portrayed by actors who are women, while
underrepresented, initiate more new conversational threads relative to their
speaking time.