  Building user trust in dialogue agents requires smooth and consistent
dialogue exchanges. However, agents can easily lose conversational context and
generate irrelevant utterances. These situations are called dialogue breakdown,
where agent utterances prevent users from continuing the conversation. Building
systems to detect dialogue breakdown allows agents to recover appropriately or
avoid breakdown entirely. In this paper we investigate the use of
semi-supervised learning methods to improve dialogue breakdown detection,
including continued pre-training on the Reddit dataset and a manifold-based
data augmentation method. We demonstrate the effectiveness of these methods on
the Dialogue Breakdown Detection Challenge (DBDC) English shared task. Our
submissions to the 2020 DBDC5 shared task place first, beating baselines and
other submissions by over 12\% accuracy. In ablations on DBDC4 data from 2019,
our semi-supervised learning methods improve the performance of a baseline BERT
model by 2\% accuracy. These methods are applicable generally to any dialogue
task and provide a simple way to improve model performance.
