Text-to-Text Pre-Training for Data-to-Text Tasks
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2020
- DOI:
- 10.48550/arXiv.2005.10433
- arXiv:
- arXiv:2005.10433
- Bibcode:
- 2020arXiv200510433K
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- Accepted to INLG-2020