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

• URL: http://arxiv.org/abs/2005.10433v3
• Date: Fri, 9 Jul 2021 00:42:32 GMT
• Title: Text-to-Text Pre-Training for Data-to-Text Tasks
• Authors: Mihir Kale, Abhinav Rastogi
• Abstract summary: 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.
• Score: 9.690158790639131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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