Text-to-text Pre-training For Data-to-text Tasks

Kale Mihir, Rastogi Abhinav. Arxiv 2020

[Paper]    
Applications BERT Fine Tuning GPT Language Modeling Model Architecture Pretraining Methods Training Techniques Transformer

We study the pre-train + fine-tune strategy for data-to-text tasks. Our experiments indicate that text-to-text pre-training in the form of T5, enables simple, end-to-end transformer based models to outperform pipelined neural architectures tailored for data-to-text generation, as well as alternative language model based pre-training techniques such as BERT and GPT-2. Importantly, T5 pre-training leads to better generalization, as evidenced by large improvements on out-of-domain test sets. We hope our work serves as a useful baseline for future research, as transfer learning becomes ever more prevalent for data-to-text tasks.

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