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Identifying breakdowns in ongoing dialogues helps to improve communicationeffectiveness. Most prior work on this topic relies on human annotated data anddata augmentation to learn a classification model. While quality labeleddialogue data requires human annotation and is usually expensive to obtain,unlabeled data is easier to collect from various sources. In this paper, wepropose a novel semi-supervised teacher-student learning framework to tacklethis task. We introduce two teachers which are trained on labeled data andperturbed labeled data respectively. We leverage unlabeled data to improveclassification in student training where we employ two teachers to refine thelabeling of unlabeled data through teacher-student learning in a bootstrappingmanner. Through our proposed training approach, the student can achieveimprovements over single-teacher performance. Experimental results on theDialogue Breakdown Detection Challenge dataset DBDC5 and Learning to IdentifyFollow-Up Questions dataset LIF show that our approach outperforms all previouspublished approaches as well as other supervised and semi-supervised baselinemethods.

A Semi-supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues