computer-code-10

SQL-to-Text

3260 papers • 126 benchmarks • 313 datasets

( Image credit: SQL-to-Text Generation with Graph-to-Sequence Model )

(Image credit: Papersgraph)

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Most implemented papers

Gated Graph Sequence Neural Networks

R. Zemel, Yujia Li, Marc Brockschmidt, Daniel Tarlow•Mon Nov 16 2015

This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

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Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

Yansong Feng, Lingfei Wu, Zhiguo Wang, Kun Xu, V. Sheinin•Mon Apr 02 2018

This work introduces a novel general end-to-end graph- to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.

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SQL-to-Text Generation with Graph-to-Sequence Model

Lingfei Wu, Mo Yu, Zhiguo Wang, V. Sheinin, Liwei Chen, Kun Xu•Thu Sep 13 2018

This paper proposes a graph-to-sequence model to encode the global structure information into node embeddings that can effectively learn the correlation between the SQL query pattern and its interpretation.

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Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play

Qi Liu, Phil Blunsom, Tao Yu, Linfeng Song, Zihuiwen Ye•Thu Oct 20 2022

Self-play improves the accuracy of a strong baseline on SParC and CoSQL, two widely used cross-domain text-to-SQL datasets, and enhances cross- domain generalization and improves beam-search.

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