# Injecting Entity Types into Entity-Guided Text Generation

arXiv: https://arxiv.org/abs/2009.13401 · HTML: https://mlsanity.com/paper/2009.13401/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Dict-BERT: Enhancing Language Model Pre-training with Dictionary](https://mlsanity.com/paper/2110.06490/) (2021)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts](https://mlsanity.com/paper/2203.07285/) (2022)
- [TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval](https://mlsanity.com/paper/2204.08173/) (2022)
- [Sentence-Permuted Paragraph Generation](https://mlsanity.com/paper/2104.07228/) (2021)
- [Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval](https://mlsanity.com/paper/2112.05917/) (2021)

## Abstract (arXiv)

Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.