  Identifying breakdowns in ongoing dialogues helps to improve communication
effectiveness. Most prior work on this topic relies on human annotated data and
data augmentation to learn a classification model. While quality labeled
dialogue data requires human annotation and is usually expensive to obtain,
unlabeled data is easier to collect from various sources. In this paper, we
propose a novel semi-supervised teacher-student learning framework to tackle
this task. We introduce two teachers which are trained on labeled data and
perturbed labeled data respectively. We leverage unlabeled data to improve
classification in student training where we employ two teachers to refine the
labeling of unlabeled data through teacher-student learning in a bootstrapping
manner. Through our proposed training approach, the student can achieve
improvements over single-teacher performance. Experimental results on the
Dialogue Breakdown Detection Challenge dataset DBDC5 and Learning to Identify
Follow-Up Questions dataset LIF show that our approach outperforms all previous
published approaches as well as other supervised and semi-supervised baseline
methods.
