Cost-sensitive active learning for dialogue state tracking

Abstract

Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data. Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information, it is very expensive to annotate all available unlabelled data. In this paper, a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies. This is the first attempt to apply active learning for dialogue state tracking. Experiments on DSTC2 show that active learning with mixed data query strategies can effectively achieve the same DST performance with significantly less data annotation compared to traditional training approaches.