Tabular question answering (TQA) presents a challenging setting for neural
systems by requiring joint reasoning of natural language with large amounts of
semi-structured data. Unlike humans who use programmatic tools like filters to
transform data before processing, language models in TQA process tables
directly, resulting in information loss as table size increases. In this paper
we propose ToolWriter to generate query specific programs and detect when to
apply them to transform tables and align them with the TQA model's
capabilities. Focusing ToolWriter to generate row-filtering tools improves the
state-of-the-art for WikiTableQuestions and WikiSQL with the most performance
gained on long tables. By investigating headroom, our work highlights the
broader potential for programmatic tools combined with neural components to
manipulate large amounts of structured data.

本文提出了 ToolWriter 算法，针对 tabular question answering 中信息丢失问题，生成查询特定的程序并检测何时将它们应用于转换表格以与 TQA 模型的能力对齐。使用 ToolWriter 生成的行过滤器工具，提高了 WikiTableQuestions 和 WikiSQL 数据集的性能。同时揭示了结构化数据中潜在的程序化工具与神经元件相结合的潜力。