Text-to-Text Pre-Training for Data-to-Text Tasks
Mihir Kale, Abhinav Rastogi · 2020
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 (Raffel et al., 2019), 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-ofdomain 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.