# Generating Fine-Grained Open Vocabulary Entity Type Descriptions

arXiv: https://arxiv.org/abs/1805.10564 · HTML: https://mlsanity.com/paper/1805.10564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2018.

## Built on by (most cited first, 3 of 3)

- [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2204.06674/) (2022)
- [Improving Entity Linking through Semantic Reinforced Entity Embeddings](https://mlsanity.com/paper/2106.08495/) (2021)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)

## Abstract (arXiv)

While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.