构建首个支持统计分析的文本转代码数据集，推动智能数据分析研究。
Sigma: A dataset for text-to-code semantic parsing with statistical analysis
- 设计新数据集SIGMA，包含6000个带Python代码标注的问题。
- 半数问题为统计分析，覆盖40种分析模式，覆盖160个数据库。
- 首次系统评估模型在统计分析任务上的表现，为后续研究提供基准。
在语义解析领域，文本转SQL和问答任务已取得显著进展，但其形式化表达（如SQL语言或基础逻辑形式）限制了对数据进行多角度分析的能力，例如统计分析。为突破这一局限并推动相关研究，本文构建了SIGMA数据集，用于支持文本到代码的语义解析与统计分析。该数据集包含6000个问题及其对应的Python代码标签，覆盖160个数据库。其中一半问题为查询类问题，返回原始格式信息；另一半为统计分析类问题，涉及对数据执行统计操作。代码标签涵盖4类查询类型和40类统计分析模式。我们使用LGESQL、SmBoP和SLSQL三种基线模型进行了评估。实验表明，采用ELECTRA的LGESQL模型在结构准确率上达到83.37%，最高；结合GraPPa和T5的SmBoP模型在执行准确率上达76.38%，表现最佳。
原文摘要 · Abstract (English)
In the domain of semantic parsing, significant progress has been achieved in Text-to-SQL and question-answering tasks, both of which focus on extracting information from data sources in their native formats. However, the inherent constraints of their formal meaning representations, such as SQL programming language or basic logical forms, hinder their ability to analyze data from various perspectives, such as conducting statistical analyses. To address this limitation and inspire research in this field, we design SIGMA, a new dataset for Text-to-Code semantic parsing with statistical analysis. SIGMA comprises 6000 questions with corresponding Python code labels, spanning across 160 databases. Half of the questions involve query types, which return information in its original format, while the remaining 50% are statistical analysis questions, which perform statistical operations on the data. The Python code labels in our dataset cover 4 types of query types and 40 types of statistical analysis patterns. We evaluated the SIGMA dataset using three different baseline models: LGESQL, SmBoP, and SLSQL. The experimental results show that the LGESQL model with ELECTRA outperforms all other models, achieving 83.37% structure accuracy. In terms of execution accuracy, the SmBoP model, when combined with GraPPa and T5, reaches 76.38%.
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