Bidirectional Attention for SQL Generation
arXiv:1801.00076
Abstract
Generating structural query language (SQL) queries from natural language is a long-standing open problem. Answering a natural language question about a database table requires modeling complex interactions between the columns of the table and the question. In this paper, we apply the synthesizing approach to solve this problem. Based on the structure of SQL queries, we break down the model to three sub-modules and design specific deep neural networks for each of them. Taking inspiration from the similar machine reading task, we employ the bidirectional attention mechanisms and character-level embedding with convolutional neural networks (CNNs) to improve the result. Experimental evaluations show that our model achieves the state-of-the-art results in WikiSQL dataset.
7 pages 3 figures
References in corpus (5)
- Improving neural networks by preventing co-adaptation of feature detectors
- Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
- Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
- SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
- An Encoder-Decoder Framework Translating Natural Language to Database Queries