Abstract
To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.- Anthology ID:
- 2022.sigdial-1.55
- Volume:
- Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2022
- Address:
- Edinburgh, UK
- Editors:
- Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 595–604
- Language:
- URL:
- https://aclanthology.org/2022.sigdial-1.55
- DOI:
- 10.18653/v1/2022.sigdial-1.55
- Cite (ACL):
- Shikib Mehri, Yasemin Altun, and Maxine Eskenazi. 2022. LAD: Language Models as Data for Zero-Shot Dialog. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 595–604, Edinburgh, UK. Association for Computational Linguistics.
- Cite (Informal):
- LAD: Language Models as Data for Zero-Shot Dialog (Mehri et al., SIGDIAL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.sigdial-1.55.pdf
- Data
- BANKING77, CLINC150, HWU64, STAR