AMR-to-text generation is used to transduce Abstract Meaning Representation
structures (AMR) into text. A key challenge in this task is to efficiently
learn effective graph representations. Previously, Graph Convolution Networks
(GCNs) were used to encode input AMRs, however, vanilla GCNs are not able to
capture non-local information and additionally, they follow a local
(first-order) information aggregation scheme. To account for these issues,
larger and deeper GCN models are required to capture more complex interactions.
In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight
Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local
interactions by synthesizing higher order information from the input graphs. We
further develop two novel parameter saving strategies based on the group graph
convolutions and weight tied convolutions to reduce memory usage and model
complexity. With the help of these strategies, we are able to train a model
with fewer parameters while maintaining the model capacity. Experiments
demonstrate that LDGCNs outperform state-of-the-art models on two benchmark
datasets for AMR-to-text generation with significantly fewer parameters.

本文介绍了轻量动态图卷积网络（LDGCNs），应用于 AMR-to-text 生成任务中。LDGCNs 采用动态融合机制，利用高阶信息从输入图中合成更丰富的非局部交互，并采用组图卷积和权重共享卷积来减少内存使用和模型复杂度，并通过实验验证了它在 AMR-to-text 生成任务中优于其他同类模型且具有显著较少的参数。