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）生成自然语言表述。虽然端到端技术存在事实正确性（忠实度）较低的问题，但先前有研究报告指出，使用手动逻辑形式（LF）表示所选内容及目标文本语义可带来性能提升。由于涉及人工步骤，自动逻辑形式是否有效、抑或改进仅源于内容选择尚不明确。我们提出TlT方法，该方法在给定表格和内容选择后，首先生成逻辑形式，再生成文本表述。我们首次证明自动逻辑形式可提升质量，相较于未使用逻辑形式的可比较系统，忠实度提高了30个百分点。我们的实验量化了实现高事实正确性所面临的剩余挑战：首要挑战是自动内容选择，其次是更优的逻辑到文本生成，最后是更优的表格到逻辑解析。