We study the pre-train + fine-tune strategy for data-to-text tasks. Fine-tuning T5 achieves state-of-the-art results on the WebNLG, MultiWoz and ToTTo benchmarks. Moreover, the models are fully end-to-end and do not rely on any intermediate planning steps, delexicalization or copy mechanisms. T5 pre-training also enables stringer 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 pre-training becomes ever more prevalent for data-to-text tasks.
We study the pre-train + fine-tune strategy for data-to-text tasks. Fine-tuning T5 achieves state-of-the-art results on the WebNLG, MultiWoz and ToTTo benchmarks. Such transfer learning enables training of fully end-to-end models that do not rely on any intermediate planning steps, delexicalization or copy mechanisms. T5 pre-training also enables stronger 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 pre-training becomes ever more prevalent for data-to-text tasks.
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