STAR: A Schema-Guided Dialog Dataset for Transfer Learning
arXiv:2010.11853
Abstract
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.
arXiv commentEqual contribution: Johannes E. M. Mosig, Shikib Mehri
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Cited by in corpus (5)
- Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
- BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
- SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems
- GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation
- Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances