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Text-to-Text Pre-Training for Data-to-Text Tasks
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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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Cited by 1 Pith paper
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Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs
A modular T5-based pipeline using RDF triples, sentence aggregation, and style transfer generates factual text with subjective interpretations from tables, achieving moderate gains over several LLM baselines.
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