@inproceedings{li-etal-2021-shot,
title = "Few-Shot Semantic Parsing for New Predicates",
author = "Li, Zhuang and
Qu, Lizhen and
Huang, Shuo and
Haffari, Gholamreza",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.109",
doi = "10.18653/v1/2021.eacl-main.109",
pages = "1281--1291",
abstract = "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.",
}
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<abstract>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.</abstract>
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%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
Markdown (Informal)
[Few-Shot Semantic Parsing for New Predicates](https://aclanthology.org/2021.eacl-main.109) (Li et al., EACL 2021)
ACL
- Zhuang Li, Lizhen Qu, Shuo Huang, and Gholamreza Haffari. 2021. Few-Shot Semantic Parsing for New Predicates. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1281–1291, Online. Association for Computational Linguistics.