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

• URL: http://arxiv.org/abs/2301.07341v1
• Date: Wed, 18 Jan 2023 07:11:56 GMT
• Title: KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information
• Authors: Hyungtak Choi, Hyeonmok Ko, Gurpreet Kaur, Lohith Ravuru, Kiranmayi Gandikota, Manisha Jhawar, Simma Dharani, Pranamya Patil
• Abstract summary: Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. It is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. We introduce a new task - DST from dialogue between users about scheduling an event (DST-S) The DST-S task is much more challenging since it requires the model to understand and track dialogue in the dialogue between users and to understand who suggested the schedule and who agreed to the proposed schedule.
• Score: 3.342637296393915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. In addition, it is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. So, we introduce a new task - DST from dialogue between users about scheduling an event (DST-USERS). The DST-USERS task is much more challenging since it requires the model to understand and track dialogue states in the dialogue between users and to understand who suggested the schedule and who agreed to the proposed schedule. To facilitate DST-USERS research, we develop dialogue datasets between users that plan a schedule. The annotated slot values which need to be extracted in the dialogue are date, time, and location. Previous approaches, such as Machine Reading Comprehension (MRC) and traditional DST techniques, have not achieved good results in our extensive evaluations. By adopting the knowledge-integrated learning method, we achieve exceptional results. The proposed model architecture combines gazetteer features and speaker information efficiently. Our evaluations of the dialogue datasets between users that plan a schedule show that our model outperforms the baseline model.

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