Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
This work addresses the problem of model efficiency and performance in AMR-to-text generation for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackles the challenge of efficiently learning graph representations for AMR-to-text generation by introducing Lightweight Dynamic Graph Convolutional Networks (LDGCNs) that capture non-local interactions, resulting in outperforming state-of-the-art models on two benchmark datasets with significantly fewer parameters.
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.