Research data supporting "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling"
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Budzianowski, P., Wen, T., & Gasic, M. (2018). Research data supporting "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling" [Dataset]. https://doi.org/10.17863/CAM.27632
Dataset contains the following json files: 1. data.json: the woz dialogue dataset, which contains the conversation users and wizards, as well as a set of coarse labels for each user turn. 2. restaurant_db.json: the Cambridge restaurant database file, containing restaurants in the Cambridge UK area and a set of attributes. 3. attraction_db.json: the Cambridge attraction database file, contining attractions in the Cambridge UK area and a set of attributes. 4. hotel_db.json: the Cambridge hotel database file, containing hotels in the Cambridge UK area and a set of attributes. 5. train_db.json: the Cambridge train (with artificial connections) database file, containing trains in the Cambridge UK area and a set of attributes. 6. hospital_db.json: the Cambridge hospital database file, contatining information about departments. 7. police_db.json: the Cambridge police station information. 8. taxi_db.json: slot-value list for taxi domain. 9. valListFile.json: list of dialogues for validation. 10. testListFile.json: list of dialogues for testing. 11. system_acts.json: system acts annotations 12. ontology.json: Data-based ontology.
The Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a collection of human-human written conversations spanning over multiple domains and topics. The dataset was collected based on the Wizard of Oz experiment on Amazon MTurk. Each dialogue contains a goal label and several exchanges between a visitor and the system. Each system turn has labels from the set of slot-value pairs representing a coarse representation of dialogue state for both user and system. There are in total 10438 dialogues.
dialogue system, dataset, wizard of oz
The data collection was funded through Google Faculty Award.
This record's DOI: https://doi.org/10.17863/CAM.27632