What Makes Data-to-Text Generation Hard for Pretrained Language Models?

@article{Keymanesh2022WhatMD,
  title={What Makes Data-to-Text Generation Hard for Pretrained Language Models?},
  author={Moniba Keymanesh and Adrian Benton and Mark Dredze},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.11505}
}
Expressing natural language descriptions of structured facts or relations – data-to-text generation (D2T) – increases the accessibility of structured knowledge repositories. Previous work (Nan et al., 2020) shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Further… 

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