Abstract
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WikiTableQuestions data set, our parser achieves a state-of-the-art accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.- Anthology ID:
- D17-1160
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1516–1526
- Language:
- URL:
- https://aclanthology.org/D17-1160
- DOI:
- 10.18653/v1/D17-1160
- Cite (ACL):
- Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017. Neural Semantic Parsing with Type Constraints for Semi-Structured Tables. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1516–1526, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (Krishnamurthy et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/landing_page/D17-1160.pdf
- Code
- allenai/pnp