Few-Shot Semantic Parsing for New Predicates

Understand

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.