ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si


Abstract
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.
Anthology ID:
2020.emnlp-main.90
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1187–1197
Language:
URL:
https://aclanthology.org/2020.emnlp-main.90
DOI:
10.18653/v1/2020.emnlp-main.90
Bibkey:
Cite (ACL):
Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, and Luo Si. 2020. ENT-DESC: Entity Description Generation by Exploring Knowledge Graph. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1187–1197, Online. Association for Computational Linguistics.
Cite (Informal):
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (Cheng et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.90.pdf
Video:
 https://slideslive.com/38938972
Code
 LiyingCheng95/EntityDescriptionGeneration
Data
ENT-DESCAGENDAWikiBio