Few-Shot Semantic Parsing for New Predicates

• URL: http://arxiv.org/abs/2101.10708v1
• Date: Tue, 26 Jan 2021 11:08:08 GMT
• Title: Few-Shot Semantic Parsing for New Predicates
• Authors: Zhuang Li, Lizhen Qu, Shuo Huang, Gholamreza Haffari
• Abstract summary: State-of-the-art neural semantics achieve less than 25% accuracy on benchmark datasets when k= 1. Our method consistently outperforms all the baselines in both one and two-shot settings.
• Score: 33.84280840107834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with 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 pre-training. As a result, our method consistently outperforms all the baselines in both one and two-shot settings.

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