Data-to-Text Generation (D2T), a classic natural language generation problem,
aims at producing fluent descriptions for structured input data, such as a
table. Existing D2T works mainly focus on describing the superficial
associative relations among entities, while ignoring the deep comparative
logical relations, such as A is better than B in a certain aspect with a
corresponding opinion, which is quite common in our daily life. In this paper,
we introduce a new D2T task named comparative logical relation generation
(CLRG). Additionally, we propose a Comparative Logic (CoLo) based text
generation method, which generates texts following specific comparative logical
relations with contrastive learning. Specifically, we first construct various
positive and negative samples by fine-grained perturbations in entities,
aspects and opinions. Then, we perform contrastive learning in the encoder
layer to have a better understanding of the comparative logical relations, and
integrate it in the decoder layer to guide the model to correctly generate the
relations. Noting the data scarcity problem, we construct a Chinese Comparative
Logical Relation Dataset (CLRD), which is a high-quality human-annotated
dataset and challenging for text generation with descriptions of multiple
entities and annotations on their comparative logical relations. Extensive
experiments show that our method achieves impressive performance in both
automatic and human evaluations.

我们提出了一种基于比较逻辑的文本生成方法，通过对比学习来生成具有特定比较逻辑关系的文本，以正确地生成关系。我们构建了一个高质量的人工注释数据集，该数据集具有多个实体的描述和与它们的比较逻辑关系的注释，并进行了广泛的实验，结果在自动评估和人工评估中都取得了令人印象深刻的性能。