# STAR: A Schema-Guided Dialog Dataset for Transfer Learning This dataset and how it came to be, along with some baseline models, are described [in this paper](https://arxiv.org/abs/2010.11853). ## Data Format Each JSON file in the `dialogues` directory contains one dialogue in the following format: | Key | Value | |----------------------------|-----------------------------------------------------------------------------------| | "AnonymizedUserWorkerID" | String that is unique for each worker but unrelated to the worker's AMT Worker ID | | "AnonymizedWizardWorkerID" | String that is unique for each worker but unrelated to the worker's AMT Worker ID | | "BatchID" | We collected dialogues in batches, identified by this ID | | "CompletionLevel" | Can be "Complete", "EarlyDisconnectDuringDialogue", or "DisconnectDuringDialogue" | | "DialogueID" | Unique ID of this dialogue | | "Events" | List of events representing the dialogue | | "FORMAT-VERSION" | | | "Scenario" | Dictionary containing information about the scenario of this dialogue | | "UserQuestionnaire" | List of question/answer pairs for questions given to the user | | "WizardQuestionnaire" | List of question/answer pairs for questions given to the wizard | ## Citation Please use the following bibtex entry if you are using STAR for your research: ``` @article{mosig2020star, author = {Johannes E. M. Mosig and Shikib Mehri and Thomas Kober}, title = "{STAR: A Schema-Guided Dialog Dataset for Transfer Learning}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language}, year = 2020, month = oct, eid = {arXiv:2010.11853}, archivePrefix = {arXiv}, eprint = {2010.11853}, primaryClass = {cs.CL}, } ```