Graph Pre-training for AMR Parsing and Generation

Xuefeng Bai, Yulong Chen, Yue Zhang


Abstract
Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure.Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively.However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge.To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs.In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training.We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks.Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model.To our knowledge, we are the first to consider pre-training on semantic graphs.
Anthology ID:
2022.acl-long.415
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6001–6015
Language:
URL:
https://aclanthology.org/2022.acl-long.415
DOI:
10.18653/v1/2022.acl-long.415
Bibkey:
Cite (ACL):
Xuefeng Bai, Yulong Chen, and Yue Zhang. 2022. Graph Pre-training for AMR Parsing and Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6001–6015, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Graph Pre-training for AMR Parsing and Generation (Bai et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2022.acl-long.415.pdf
Software:
 2022.acl-long.415.software.zip
Code
 muyeby/amrbart
Data
BioLDC2017T10LDC2020T02New3The Little Prince