Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.

翻译：逻辑表格到文本（LT2T）生成任务旨在从表格生成逻辑上忠实的句子。当前该领域存在两大挑战：1）忠实性：如何根据表格内容生成事实正确的句子；2）多样性：如何生成多个从不同视角描述表格的句子。本文提出LoFT模型，利用逻辑形式作为事实验证器和内容规划器来控制LT2T生成过程。在LogicNLG数据集上的实验结果表明，LoFT是首个同时解决不忠实性和缺乏多样性问题的模型。我们的代码已公开于https://github.com/Yale-LILY/LoFT。