Papers › GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation

GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation

13 Apr 2022COLING 2022 10arXiv:2204.06674archive 2025-07-28

Anthony Colas, Mehrdad Alvandipour, Daisy Zhe Wang

Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks. We do so by proposing a mask structure to capture neighborhood information and a novel type encoder that adds a bias to the graph-attention weights depending on the connection type. Experiments on two KG-to-text benchmark datasets show our models are competitive while involving fewer parameters and no additional pre-training tasks. By formulating the problem as a framework, we can interchange the various proposed components and begin interpreting KG-to-text generative models based on the topological and type information found in a graph.

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