We present STAR, a schema-guided task-oriented dialog dataset consisting of 127,833 utterances and knowledge base queries across 5,820 task-oriented dialogs in 13 domains that is especially designed to facilitate task and domain transfer learning in task-oriented dialog. Furthermore, we propose a scalable crowd-sourcing paradigm to collect arbitrarily large datasets of the same quality as STAR. Moreover, we introduce novel schema-guided dialog models that use an explicit description of the task(s) to generalize from known to unknown tasks. We demonstrate the effectiveness of these models, particularly for zero-shot generalization across tasks and domains.
@article{arxiv.2010.11853,
title = {STAR: A Schema-Guided Dialog Dataset for Transfer Learning},
author = {Johannes E. M. Mosig and Shikib Mehri and Thomas Kober},
journal= {arXiv preprint arXiv:2010.11853},
year = {2020}
}
Comments
Equal contribution: Johannes E. M. Mosig, Shikib Mehri