数据库自然语言接口的语法引导神经模型

Florin Brad, R. Iacob, Ionel-Alexandru Hosu, Stefan Ruseti, Traian Rebedea
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引用次数: 1

摘要

神经代码生成的最新进展是结合语法来改进基于用户自然语言请求的目标代码生成。我们通过考虑数据库模式,使[1]模型适应于数据库的自然语言接口(NLIDB)问题。我们在最近引入的WIKISQL和SENLIDB数据集上评估了我们的模型。我们的结果表明,语法引导的模型在WIKISQL上优于简单的序列到序列(SEQ2SEQ)基线,但由于其复杂性,在SENLIDB数据集上存在问题。
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A Syntax-Guided Neural Model for Natural Language Interfaces to Databases
Recent advances in neural code generation have incorporated syntax to improve the generation of the target code based on the user's request in natural language. We adapt the model of [1] to the Natural Language Interface to Databases (NLIDB) problem by taking into account the database schema. We evaluate our model on the recently introduced WIKISQL and SENLIDB datasets. Our results show that the syntax-guided model outperforms a simple sequence-to-sequence (SEQ2SEQ) baseline on WIKISQL, but has trouble with the SENLIDB dataset due to its complexity.
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