Abstract
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.- Anthology ID:
- 2023.findings-emnlp.1051
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15700–15710
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.1051/
- DOI:
- 10.18653/v1/2023.findings-emnlp.1051
- Cite (ACL):
- Jie Huang and Kevin Chang. 2023. VER: Unifying Verbalizing Entities and Relations. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15700–15710, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- VER: Unifying Verbalizing Entities and Relations (Huang & Chang, Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.1051.pdf