  Entities and relationships between entities are vital in the real world.
Essentially, we understand the world by understanding entities and relations.
For instance, to understand a field, e.g., computer science, we need to
understand the relevant concepts, e.g., machine learning, and the relationships
between concepts, e.g., machine learning and artificial intelligence. To
understand a person, we should first know who he/she is and how he/she is
related to others. To understand entities and relations, humans may refer to
natural language descriptions. For instance, when learning a new scientific
term, people usually start by reading its definition in dictionaries or
encyclopedias. To know the relationship between two entities, humans tend to
create a sentence to connect them. In this paper, we propose VER: a unified
model for Verbalizing Entities and Relations. Specifically, we attempt to build
a system that takes any entity or entity set as input and generates a sentence
to represent entities and relations. Extensive experiments demonstrate that our
model can generate high-quality sentences describing entities and entity
relationships and facilitate various tasks on entities and relations, including
definition modeling, relation modeling, and generative commonsense reasoning.
