  Many modern messaging systems allow fast and synchronous textual
communication among many users. The resulting sequence of messages hides a more
complicated structure in which independent sub-conversations are interwoven
with one another. This poses a challenge for any task aiming to understand the
content of the chat logs or gather information from them. The ability to
disentangle these conversations is then tantamount to the success of many
downstream tasks such as summarization and question answering. Structured
information accompanying the text such as user turn, user mentions, timestamps,
is used as a cue by the participants themselves who need to follow the
conversation and has been shown to be important for disentanglement. DAG-LSTMs,
a generalization of Tree-LSTMs that can handle directed acyclic dependencies,
are a natural way to incorporate such information and its non-sequential
nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement
task. We perform our experiments on the Ubuntu IRC dataset. We show that the
novel model we propose achieves state of the art status on the task of
recovering reply-to relations and it is competitive on other disentanglement
metrics.
