论文标题
关于语义解析SQL查询的词典逻辑对准的潜力
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
论文作者
论文摘要
带有逻辑形式注释的大规模语义解析数据集已使监督方法取得了重大进展。但是，富裕的监督可以提供更多帮助吗？为了探索细粒度，词汇水平的监督的实用性，我们引入了Squall，这是一个数据集，该数据集丰富了11,276个Wikikitable英语问题，并具有手动创建的SQL等效物以及SQL和问题片段之间的对齐方式。我们的注释为编码器模型提供了新的培训可能性，包括先前缺乏对齐的机器翻译的方法。我们提出并测试两种方法：（1）监督注意力； （2）采用辅助目标，即在表列的输入查询中删除参考文献。在5倍的交叉验证中，这些策略对强基础的执行精度提高了4.4％。 Oracle实验表明，带注释的对准可以支持高达23.9％的进一步准确性提高。
Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the utility of fine-grained, lexical-level supervision, we introduce Squall, a dataset that enriches 11,276 WikiTableQuestions English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. Our annotation enables new training possibilities for encoder-decoder models, including approaches from machine translation previously precluded by the absence of alignments. We propose and test two methods: (1) supervised attention; (2) adopting an auxiliary objective of disambiguating references in the input queries to table columns. In 5-fold cross validation, these strategies improve over strong baselines by 4.4% execution accuracy. Oracle experiments suggest that annotated alignments can support further accuracy gains of up to 23.9%.
