@inproceedings{saparov-etal-2017-probabilistic,
title = "A Probabilistic Generative Grammar for Semantic Parsing",
author = "Saparov, Abulhair and
Saraswat, Vijay and
Mitchell, Tom",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1026",
doi = "10.18653/v1/K17-1026",
pages = "248--259",
abstract = "We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical forms. We develop a semantic parser that finds the logical form with the highest posterior probability exactly. We obtain strong results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.",
}
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%0 Conference Proceedings
%T A Probabilistic Generative Grammar for Semantic Parsing
%A Saparov, Abulhair
%A Saraswat, Vijay
%A Mitchell, Tom
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F saparov-etal-2017-probabilistic
%X We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical forms. We develop a semantic parser that finds the logical form with the highest posterior probability exactly. We obtain strong results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.
%R 10.18653/v1/K17-1026
%U https://aclanthology.org/K17-1026
%U https://doi.org/10.18653/v1/K17-1026
%P 248-259
Markdown (Informal)
[A Probabilistic Generative Grammar for Semantic Parsing](https://aclanthology.org/K17-1026) (Saparov et al., CoNLL 2017)
ACL
- Abulhair Saparov, Vijay Saraswat, and Tom Mitchell. 2017. A Probabilistic Generative Grammar for Semantic Parsing. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 248–259, Vancouver, Canada. Association for Computational Linguistics.