VER: Unifying Verbalizing Entities and Relations
This work addresses the need for automated verbalization of entities and relations, which is incremental as it builds on existing methods for natural language generation in knowledge representation.
The authors tackled the problem of generating natural language descriptions for entities and their relationships, proposing VER, a unified model that takes entities as input and produces sentences representing them and their relations, with experiments showing it generates high-quality sentences and aids tasks like definition modeling and commonsense reasoning.
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.