Text-to-Text Pre-Training for Data-to-Text Tasks

Mihir Kale; Abhinav Rastogi

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arXiv · 2005.10433

Text-to-Text Pre-Training for Data-to-Text Tasks

Mihir Kale·Abhinav Rastogi

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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, 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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BibTeX RIS

Mihir Kale, Abhinav Rastogi. 2020-05-21. Text-to-Text Pre-Training for Data-to-Text Tasks. https://arxiv.org/abs/2005.10433

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