Text-to-Text Pre-Training for Data-to-Text Tasks
It is indicated 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 alternatives such as BERT and GPT-2.
Abstract
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 alternatives 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.