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
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N18-2115.pdf
Note:
 N18-2115.Notes.pdf
Code
 Microsoft/PointerSQL
Data
WikiSQL