Injecting Entity Types into Entity-Guided Text Generation
Xiangyu Dong, Wenhao Yu, Chenguang Zhu, Meng Jiang
Abstract
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.
BibTeX
@inproceedings{dong-etal-2021-injecting,
title = "Injecting Entity Types into Entity-Guided Text Generation",
author = "Dong, Xiangyu and
Yu, Wenhao and
Zhu, Chenguang and
Jiang, Meng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.56/",
doi = "10.18653/v1/2021.emnlp-main.56",
pages = "734--741"
}