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# 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},
}
``` |