Structural Adapters in Pretrained Language Models for AMR-to-Text Generation

Leonardo F. R. Ribeiro, Yue Zhang, Iryna Gurevych


Abstract
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters.
Anthology ID:
2021.emnlp-main.351
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4269–4282
Language:
URL:
https://aclanthology.org/2021.emnlp-main.351
DOI:
10.18653/v1/2021.emnlp-main.351
Bibkey:
Cite (ACL):
Leonardo F. R. Ribeiro, Yue Zhang, and Iryna Gurevych. 2021. Structural Adapters in Pretrained Language Models for AMR-to-Text Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4269–4282, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation (Ribeiro et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.351.pdf
Code
 ukplab/structadapt
Data
AMR3.0LDC2020T02