论文标题
在语义解析器的输出中搜索更好的数据库查询
Searching for Better Database Queries in the Outputs of Semantic Parsers
论文作者
论文摘要
从自然语言问题中生成数据库查询的任务遭受了歧义和目标的精确描述。当系统需要概括到培训中看不见的数据库时，问题就会放大。在本文中，我们考虑了在测试时间内系统可以访问评估生成查询的外部标准的情况。该标准可以通过检查查询执行是否执行而没有错误来验证一组测试中的查询而有所不同。在这种情况下，我们使用搜索算法来增强神经回归模型，该算法寻找满足标准的查询。我们将方法应用于最新的语义解析器，并报告说，它使我们能够找到许多通过不同数据集中所有测试的查询。
The task of generating a database query from a question in natural language suffers from ambiguity and insufficiently precise description of the goal. The problem is amplified when the system needs to generalize to databases unseen at training. In this paper, we consider the case when, at the test time, the system has access to an external criterion that evaluates the generated queries. The criterion can vary from checking that a query executes without errors to verifying the query on a set of tests. In this setting, we augment neural autoregressive models with a search algorithm that looks for a query satisfying the criterion. We apply our approach to the state-of-the-art semantic parsers and report that it allows us to find many queries passing all the tests on different datasets.
