  Dialogue state tracking (DST) plays an important role in task-oriented
dialogue systems. However, collecting a large amount of turn-by-turn annotated
dialogue data is costly and inefficient. In this paper, we propose a novel
turn-level active learning framework for DST to actively select turns in
dialogues to annotate. Given the limited labelling budget, experimental results
demonstrate the effectiveness of selective annotation of dialogue turns.
Additionally, our approach can effectively achieve comparable DST performance
to traditional training approaches with significantly less annotated data,
which provides a more efficient way to annotate new dialogue data.
