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.- Anthology ID:
- 2021.eacl-main.109
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1281–1291
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.109
- DOI:
- 10.18653/v1/2021.eacl-main.109
- Cite (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.
- Cite (Informal):
- Few-Shot Semantic Parsing for New Predicates (Li et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.eacl-main.109.pdf
- Code
- zhuang-li/few-shot-semantic-parsing