Turn-Level Active Learning for Dialogue State Tracking
Zihan Zhang, Meng Fang, Fanghua Ye, Ling Chen, Mohammad‐Reza Namazi‐Rad · 2023
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.However, collecting a large amount of turnby-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 1 . Turn User SystemCan you tell me some info on the Avalon hotel?The Avalon is a 4 star moderately priced guesthouse in the north with free internet.Would you like to book there?Yes. Can you book it for 5 people on Saturday?We need rooms for 4 nights. Dialogue StateYour taxi has been booked to take you from Avalon to Frankie and Bennys at 17:45.Your taxi will be a black Tesla and the contact number is 07715682347.That sounds great.Thank you very much.…..