Enhancing AMR-to-Text Generation with Dual Graph Representations

Leonardo F. R. Ribeiro, Claire Gardent, Iryna Gurevych


Abstract
Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges. To address this difficulty, we propose a novel graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the AMR graph. The model learns parallel top-down and bottom-up representations of nodes capturing contrasting views of the graph. We also investigate the use of different node message passing strategies, employing different state-of-the-art graph encoders to compute node representations based on incoming and outgoing perspectives. In our experiments, we demonstrate that the dual graph representation leads to improvements in AMR-to-text generation, achieving state-of-the-art results on two AMR datasets
Anthology ID:
D19-1314
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3183–3194
Language:
URL:
https://aclanthology.org/D19-1314
DOI:
10.18653/v1/D19-1314
Bibkey:
Cite (ACL):
Leonardo F. R. Ribeiro, Claire Gardent, and Iryna Gurevych. 2019. Enhancing AMR-to-Text Generation with Dual Graph Representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3183–3194, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Enhancing AMR-to-Text Generation with Dual Graph Representations (Ribeiro et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1314.pdf
Attachment:
 D19-1314.Attachment.zip
Code
 UKPLab/emnlp2019-dualgraph
Data
LDC2017T10