@inproceedings{huang2018natural,
author = {Huang, Po-Sen and Wang, Chenglong and Singh, Rishabh and Yih, Wen-tau and He, Xiaodong},
title = {Natural Language to Structured Query Generation via Meta-Learning},
booktitle = {NAACL HLT 2018},
year = {2018},
month = {June},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/natural-language-to-structured-query-generation-via-meta-learning/},
edition = {NAACL HLT 2018},
}