Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

Understand

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation.

• It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented.
• This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach.
• Results show that semi-supervised learning results in higher scores on diversity metrics.