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Dialogue State Tracking (DST) is core research in dialogue systems and hasreceived much attention. In addition, it is necessary to define a new problemthat can deal with dialogue between users as a step toward the conversationalAI that extracts and recommends information from the dialogue between users.So, we introduce a new task - DST from dialogue between users about schedulingan event (DST-USERS). The DST-USERS task is much more challenging since itrequires the model to understand and track dialogue states in the dialoguebetween users and to understand who suggested the schedule and who agreed tothe proposed schedule. To facilitate DST-USERS research, we develop dialoguedatasets between users that plan a schedule. The annotated slot values whichneed to be extracted in the dialogue are date, time, and location. Previousapproaches, such as Machine Reading Comprehension (MRC) and traditional DSTtechniques, have not achieved good results in our extensive evaluations. Byadopting the knowledge-integrated learning method, we achieve exceptionalresults. The proposed model architecture combines gazetteer features andspeaker information efficiently. Our evaluations of the dialogue datasetsbetween users that plan a schedule show that our model outperforms the baselinemodel.

KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information