Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL representations. One of the most intractable problem of conversational text-to-SQL is modeling the semantics of multi-turn queries and gathering proper information required for the current query. This paper shows that explicit modeling the semantic changes by adding each turn and the summarization of the whole context can bring better performance on converting conversational queries into SQLs. In particular, we propose two conversational modeling tasks in both turn grain and conversation grain. These two tasks simply work as auxiliary training tasks to help with multi-turn conversational semantic parsing. We conducted empirical studies and achieve new state-of-the-art results on large-scale open-domain conversational text-to-SQL dataset. The results demonstrate that the proposed mechanism significantly improves the performance of multi-turn semantic parsing.
翻译:连接文本到 SQL 的目的是将多方向自然语言查询转换为相应的 SQL 表达方式。 最棘手的对等文本到 SQL 问题之一是模拟多方向查询的语义,并收集当前查询所需的适当信息。 本文显示,通过增加每个转折和对全背景的概括化来明确模拟语义变化,可以提高将对口查询转换为 SQL 的性能。 特别是, 我们提议在转基因谷物和对口谷物中执行两个对口模式任务。 这两项任务只是作为辅助培训任务,协助多方向对口语义解析。 我们进行了经验性研究,并在大型开放式对口文字到 SQL 数据集上取得了新的最新结果。 结果显示,提议的机制大大改善了多方向语义解析的性能。