Generating natural language statements to convey logical inferences from tabular data (i.e., Logical NLG) is a process with one input and a variety of valid outputs. This characteristic underscores the need for a method to produce a diverse set of valid outputs, presenting different perspectives of the input data. We propose a simple yet effective diversity-enhancing scheme that builds upon an inherent property of the statements, their logic-types, by using a type-controlled table-to-text generation model. We demonstrate, through extensive automatic and human evaluations over the two publicly available Logical NLG datasets, that our proposed method both facilitates the ability to effectively control the generated statement type, and produces results superior to the strongest baselines in terms of quality and factuality-diversity trade-off.

翻译：从表格数据中生成自然语言陈述以传达逻辑推理（即逻辑型自然语言生成）是一个输入对应多种有效输出的过程。这一特性凸显了需要一种方法，用于生成多样化的有效输出集，从不同视角呈现输入数据。我们提出了一种简单而有效的多样性增强方案，该方案基于陈述的内在属性——其逻辑类型，通过使用类型控制的表格到文本生成模型。通过在两个公开可用的逻辑型自然语言生成数据集上进行广泛的自动评估和人工评估，我们证明了所提出的方法不仅增强了有效控制生成陈述类型的能力，而且在质量和事实性-多样性权衡方面产生了优于最强基线的结果。