Few-Shot Semantic Parsing for New Predicates
Zhuang Li, Lizhen Qu, Shuo Huang, Gholamreza Haffari · 2021
In this work, we investigate the problems of semantic parsing in a few-shot learning setting.In this setting, we are provided with k utterance-logical form pairs per new predicate.The state-of-the-art neural semantic parsers achieve less than 25% accuracy on benchmark datasets when k = 1.To tackle this problem, we proposed to i) apply a designated metalearning method to train the model; ii) regularize attention scores with alignment statistics; iii) apply a smoothing technique in pretraining.As a result, our method consistently outperforms all the baselines in both one and two-shot settings.