Natural Language to Structured Query Generation via Meta-Learning

Understand

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.