论文标题
多样性通过类型控制增强了桌面到文本的生成
Diversity Enhanced Table-to-Text Generation via Type Control
论文作者
论文摘要
生成自然语言语句从表格数据（即逻辑NLG）传达逻辑推断是一个输入和各种有效输出的过程。这种特征强调了一种方法需要产生各种有效输出的方法，从而呈现了输入数据的不同观点。我们提出了一种简单而有效的多样性增强方案，该方案通过使用类型控制的表格到文本生成模型建立在陈述固有属性，其逻辑类型的基于其逻辑类型的基础上。我们通过对两个公开可用的逻辑NLG数据集进行了广泛的自动和人类评估来证明，我们提出的方法既有助于有效地控制生成的陈述类型，并且在质量和事实性多样性方面产生的结果优于最强的基准。
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.
