We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.
翻译:我们在一个互动环境中研究语义分析,用户在这种环境中纠正自然语言反馈错误。我们介绍了NL-EDIT,这是在互动背景下解释自然语言反馈的模式,以产生一系列编辑,可用于最初的解析,纠正错误。我们显示NL-EDIT可以提高现有文本到SQL的精确度,最多提高20%,只有一转更正。我们分析了模型的局限性,并讨论了改进和评价的方向。本文使用的代码和数据集可在http://aka.ms/NLEdit上公开查阅。