Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model

Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou


Abstract
Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are two essential tasks for knowledge graphs. Existing unsupervised approaches become suitable candidates for jointly learning the two tasks due to their avoidance of using graph-text parallel data. However, they adopt multiple complex modules and still require entity information or relation type for training. To this end, we propose INFINITY, a simple yet effective unsupervised method with a unified pretrained language model that does not introduce external annotation tools or additional parallel information. It achieves fully unsupervised graph-text mutual conversion for the first time. Specifically, INFINITY treats both G2T and T2G as a bidirectional sequence generation task by fine-tuning only one pretrained seq2seq model. A novel back-translation-based framework is then designed to generate synthetic parallel data automatically. Besides, we investigate the impact of graph linearization and introduce the structure-aware fine-tuning strategy to alleviate possible performance deterioration via retaining structural information in graph sequences. As a fully unsupervised framework, INFINITY is empirically verified to outperform state-of-the-art baselines for G2T and T2G tasks. Additionally, we also devise a new training setting called cross learning for low-resource unsupervised information extraction.
Anthology ID:
2023.acl-long.281
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5130–5144
Language:
URL:
https://aclanthology.org/2023.acl-long.281
DOI:
10.18653/v1/2023.acl-long.281
Bibkey:
Cite (ACL):
Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, and Chenghu Zhou. 2023. Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5130–5144, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (Xu et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.281.pdf
Video:
 https://aclanthology.org/2023.acl-long.281.mp4