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arXiv · 1909.00352

Enhancing AMR-to-Text Generation with Dual Graph Representations

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

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Leonardo F. R. Ribeiro, Claire Gardent, Iryna Gurevych. 2019-09-01. Enhancing AMR-to-Text Generation with Dual Graph Representations. https://arxiv.org/abs/1909.00352

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