Constrained Language Models Yield Few-Shot Semantic Parsers
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein
Abstract
We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.
BibTeX
@inproceedings{shin-etal-2021-constrained,
title = "Constrained Language Models Yield Few-Shot Semantic Parsers",
author = "Shin, Richard and
Lin, Christopher and
Thomson, Sam and
Chen, Charles and
Roy, Subhro and
Platanios, Emmanouil Antonios and
Pauls, Adam and
Klein, Dan and
Eisner, Jason and
Van Durme, Benjamin",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.608/",
doi = "10.18653/v1/2021.emnlp-main.608",
pages = "7699--7715"
}