Abstract
Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.- Anthology ID:
- 2021.sigdial-1.52
- Volume:
- Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2021
- Address:
- Singapore and Online
- Editors:
- Haizhou Li, Gina-Anne Levow, Zhou Yu, Chitralekha Gupta, Berrak Sisman, Siqi Cai, David Vandyke, Nina Dethlefs, Yan Wu, Junyi Jessy Li
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 499–508
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2021.sigdial-1.52/
- DOI:
- 10.18653/v1/2021.sigdial-1.52
- Cite (ACL):
- Shikib Mehri and Maxine Eskenazi. 2021. Schema-Guided Paradigm for Zero-Shot Dialog. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 499–508, Singapore and Online. Association for Computational Linguistics.
- Cite (Informal):
- Schema-Guided Paradigm for Zero-Shot Dialog (Mehri & Eskenazi, SIGDIAL 2021)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2021.sigdial-1.52.pdf
- Code
- Shikib/schema_attention_model
- Data
- STAR