Investigating Pretrained Language Models for Graph-to-Text Generation
Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively.
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] METEOR', '[BOLD] chrF++', '[BOLD] BS (F1)', '[BOLD] MS', '[BOLD] BT']
We compare our approaches against several recent results reported on this dataset. It is worth mentioning that AGENDA graphs are incomplete regarding the target text. In contrast to WebNLG, this dataset is from scientific domain and the KGs and texts are extracted from the AI domain koncel-kedziorski-etal-2019-text. The models also have strong performance on this dataset. We believe that their capacity to generate fluent text helps when generating paper abstracts, even though they were not pretrained in the scientific domain. We note that an intermediate task-adaptive pretraining can enable the models to achieve better performance. Finally, note that BART has impressive performance with its large variant achieving a score of 23.65 BLEU points, 5.6 points higher than the previous state of the art.