Stage-wise Fine-tuning for Graph-to-Text Generation

Qingyun Wang, Semih Yavuz, Xi Victoria Lin, Heng Ji, Nazneen Rajani


Abstract
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize the structure information of the input graph. In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph-to-text generation. In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel tree-level embedding method to capture the inter-dependency structures of the input graph. This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.
Anthology ID:
2021.acl-srw.2
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16–22
Language:
URL:
https://aclanthology.org/2021.acl-srw.2
DOI:
10.18653/v1/2021.acl-srw.2
Bibkey:
Cite (ACL):
Qingyun Wang, Semih Yavuz, Xi Victoria Lin, Heng Ji, and Nazneen Rajani. 2021. Stage-wise Fine-tuning for Graph-to-Text Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 16–22, Online. Association for Computational Linguistics.
Cite (Informal):
Stage-wise Fine-tuning for Graph-to-Text Generation (Wang et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-srw.2.pdf
Optional supplementary material:
 2021.acl-srw.2.OptionalSupplementaryMaterial.pdf
Video:
 https://aclanthology.org/2021.acl-srw.2.mp4
Code
 EagleW/Stage-wise-Fine-tuning
Data
WebNLG