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• W3122380889 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." @default.
• W3122380889 created "2021-02-01" @default.
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• W3122380889 date "2021-01-26" @default.
• W3122380889 modified "2023-10-16" @default.
• W3122380889 title "Few-Shot Semantic Parsing for New Predicates" @default.
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• W3122380889 doi "https://doi.org/10.48550/arxiv.2101.10708" @default.
• W3122380889 hasPublicationYear "2021" @default.
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