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

Mihir Kale, Abhinav Rastogi


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.
Anthology ID:
2020.inlg-1.14
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–102
Language:
URL:
https://www.aclweb.org/anthology/2020.inlg-1.14
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.inlg-1.14.pdf