Table-to-text systems generate natural language statements from structured data like tables. While end-to-end techniques suffer from low factual correctness (fidelity), a previous study reported gains when using manual logical forms (LF) that represent the selected content and the semantics of the target text. Given the manual step, it was not clear whether automatic LFs would be effective, or whether the improvement came from content selection alone. We present TlT which, given a table and a selection of the content, first produces LFs and then the textual statement. We show for the first time that automatic LFs improve quality, with an increase in fidelity of 30 points over a comparable system not using LFs. Our experiments allow to quantify the remaining challenges for high factual correctness, with automatic selection of content coming first, followed by better Logic-to-Text generation and, to a lesser extent, better Table-to-Logic parsing.

翻译：表格到文本系统从结构化数据如表格中生成自然语言语句。虽然端到端技术存在事实正确性（忠实度）低的问题，但先前的一项研究报告了在使用手动逻辑形式（LF）表示选定内容和目标文本语义时取得的改进。由于手动步骤的存在，尚不清楚自动逻辑形式是否有效，或者改进是否仅来自内容选择。我们提出TlT，该系统在给定表格和内容选择后，首先生成逻辑形式，然后生成文本语句。我们首次证明自动逻辑形式能提升质量，与不使用逻辑形式的类似系统相比，忠实度提高了30个点。我们的实验得以量化实现高事实正确性所面临的剩余挑战，其中自动内容选择为首要问题，其次是更好的逻辑到文本生成，以及影响较小的更好的表格到逻辑解析。