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        <title>Neural Semantic Parsing with Type Constraints for Semi-Structured Tables</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
%A Krishnamurthy, Jayant
%A Dasigi, Pradeep
%A Gardner, Matt
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F krishnamurthy-etal-2017-neural
%X 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.
%R 10.18653/v1/D17-1160
%U https://aclanthology.org/D17-1160
%U https://doi.org/10.18653/v1/D17-1160
%P 1516-1526

Markdown (Informal)

[Neural Semantic Parsing with Type Constraints for Semi-Structured Tables](https://aclanthology.org/D17-1160) (Krishnamurthy et al., EMNLP 2017)

• Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (Krishnamurthy et al., EMNLP 2017)

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.