# VER: Unifying Verbalizing Entities and Relations

arXiv: https://arxiv.org/abs/2211.11093 · HTML: https://mlsanity.com/paper/2211.11093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning](https://mlsanity.com/paper/2009.12677/) (2020)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Structural Deep Embedding for Hyper-Networks](https://mlsanity.com/paper/1711.10146/) (2017)
- [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding](https://mlsanity.com/paper/1602.05307/) (2016)
- [An Enhanced Knowledge Injection Model for Commonsense Generation](https://mlsanity.com/paper/2012.00366/) (2020)
- [Retrieval Enhanced Model for Commonsense Generation](https://mlsanity.com/paper/2105.11174/) (2021)
- [Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling](https://mlsanity.com/paper/1911.05715/) (2019)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling](https://mlsanity.com/paper/2010.03124/) (2020)
- [Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning](https://mlsanity.com/paper/2210.11708/) (2022)
- [Open Relation Modeling: Learning to Define Relations between Entities](https://mlsanity.com/paper/2108.09241/) (2021)
- [Contextualized Scene Imagination for Generative Commonsense Reasoning](https://mlsanity.com/paper/2112.06318/) (2021)
- [DEER: Descriptive Knowledge Graph for Explaining Entity Relationships](https://mlsanity.com/paper/2205.10479/) (2022)
- [Hyper-Path-Based Representation Learning for Hyper-Networks](https://mlsanity.com/paper/1908.09152/) (2019)
- [KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning](https://mlsanity.com/paper/2109.06704/) (2021)

## Built on by (most cited first, 2 of 2)

- [DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships](https://mlsanity.com/paper/2212.10545/) (2022)
- [Descriptive Knowledge Graph in Biomedical Domain](https://mlsanity.com/paper/2310.11681/) (2023)

## Abstract (arXiv)

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.