Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. To tackle these problems, we propose a graph-text joint representation learning model called JointGT. During encoding, we devise a structure-aware semantic aggregation module which is plugged into each Transformer layer to preserve the graph structure. Furthermore, we propose three new pre-training tasks to explicitly enhance the graph-text alignment including respective text / graph reconstruction, and graph-text alignment in the embedding space via Optimal Transport. Experiments show that JointGT obtains new state-of-the-art performance on various KG-to-text datasets.
@article{arxiv.2106.10502,
title = {JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs},
author = {Pei Ke and Haozhe Ji and Yu Ran and Xin Cui and Liwei Wang and Linfeng Song and Xiaoyan Zhu and Minlie Huang},
journal= {arXiv preprint arXiv:2106.10502},
year = {2021}
}