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.- Anthology ID:
- K17-1026
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Roger Levy, Lucia Specia
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 248–259
- Language:
- URL:
- https://aclanthology.org/K17-1026
- DOI:
- 10.18653/v1/K17-1026
- Cite (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.
- Cite (Informal):
- A Probabilistic Generative Grammar for Semantic Parsing (Saparov et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/K17-1026.pdf
- Code
- asaparov/parser + additional community code