%0 Conference Proceedings %T Few-Shot Semantic Parsing for New Predicates %A Li, Zhuang %A Qu, Lizhen %A Huang, Shuo %A Haffari, Gholamreza %Y Merlo, Paola %Y Tiedemann, Jorg %Y Tsarfaty, Reut %S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume %D 2021 %8 April %I Association for Computational Linguistics %C Online %F li-etal-2021-shot %X 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 meta-learning 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. %R 10.18653/v1/2021.eacl-main.109 %U https://aclanthology.org/2021.eacl-main.109 %U https://doi.org/10.18653/v1/2021.eacl-main.109 %P 1281-1291