Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He
Abstract
In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%–5.4% absolute accuracy gains over the non-meta-learning counterparts.- Anthology ID:
- N18-2115
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 732–738
- Language:
- URL:
- https://aclanthology.org/N18-2115
- DOI:
- 10.18653/v1/N18-2115
- Cite (ACL):
- Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, and Xiaodong He. 2018. Natural Language to Structured Query Generation via Meta-Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 732–738, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Natural Language to Structured Query Generation via Meta-Learning (Huang et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/N18-2115.pdf
- Code
- Microsoft/PointerSQL
- Data
- WikiSQL