Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Krishnamurthy, Jayant; Dasigi, Pradeep; Gardner, Matt

doi:10.18653/v1/d17-1160

Cited by 201 publications

(165 citation statements)

References 20 publications

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“…We perform a simple modification to . We add cell values to the graph, in a similar fashion to Krishnamurthy et al (2017). Specifically, we extract the cells of the first 5000 rows of all tables in the schema, during the pre-processing phase.…”

Section: A Re-implementationmentioning

confidence: 99%

Global Reasoning over Database Structures for Text-to-SQL Parsing

Bogin

Gardner²,

Berant

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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State-of-the-art semantic parsers rely on autoregressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zeroshot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding. In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextuallyinformed selection of database constants. We use message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question. Moreover, we train a model to rank queries based on the global alignment of database constants to question words. We apply our techniques to the current state-ofthe-art model for SPIDER, a zero-shot semantic parsing dataset with complex databases, increasing accuracy from 39.4% to 47.4%.

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Section: A Re-implementationmentioning

confidence: 99%

Global Reasoning over Database Structures for Text-to-SQL Parsing

Bogin

Gardner²,

Berant

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

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“…Cheng et al (2017) and Dong and Lapata (2018) both try to decode in two steps, from a coarse rough sketch to a finer structure hierarchically. Krishnamurthy et al (2017) define a grammar of production rules such that only welltyped logical forms can be generated. Yin and Neubig (2017) and Chen et al (2018a) both transform the generation of logical forms into query graph construction.…”

Section: Related Workmentioning

confidence: 99%

Semantic Parsing with Dual Learning

Cao¹,

Zhu²,

Liu³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.

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“…• Syn Dep (Dua et al, 2019), the neural semantic parsing model (KDG) (Krishnamurthy et al, 2017) with Stanford dependencies based sentence representations;…”

Section: Baselinesmentioning

confidence: 99%

NumNet: Machine Reading Comprehension with Numerical Reasoning

Ran¹,

Lin²,

Li³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56% on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.

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Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Cited by 201 publications

References 20 publications

Global Reasoning over Database Structures for Text-to-SQL Parsing

Global Reasoning over Database Structures for Text-to-SQL Parsing

Semantic Parsing with Dual Learning

NumNet: Machine Reading Comprehension with Numerical Reasoning

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