  Conversation disentanglement aims to separate intermingled messages into
detached sessions, which is a fundamental task in understanding multi-party
conversations. Existing work on conversation disentanglement relies heavily
upon human-annotated datasets, which are expensive to obtain in practice. In
this work, we explore to train a conversation disentanglement model without
referencing any human annotations. Our method is built upon a deep co-training
algorithm, which consists of two neural networks: a message-pair classifier and
a session classifier. The former is responsible for retrieving local relations
between two messages while the latter categorizes a message to a session by
capturing context-aware information. Both networks are initialized respectively
with pseudo data built from an unannotated corpus. During the deep co-training
process, we use the session classifier as a reinforcement learning component to
learn a session assigning policy by maximizing the local rewards given by the
message-pair classifier. For the message-pair classifier, we enrich its
training data by retrieving message pairs with high confidence from the
disentangled sessions predicted by the session classifier. Experimental results
on the large Movie Dialogue Dataset demonstrate that our proposed approach
achieves competitive performance compared to the previous supervised methods.
Further experiments show that the predicted disentangled conversations can
promote the performance on the downstream task of multi-party response
selection.
