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arXiv · 2210.09162

Table-To-Text generation and pre-training with TabT5

Ewa Andrejczuk·Julian Martin Eisenschlos·Francesco Piccinno·Syrine Krichene·Yasemin Altun
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Abstract

Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020). A major limitation of these architectures is that they are constrained to classification-like tasks such as cell selection or entailment detection. We present TABT5, an encoder-decoder model that generates natural language text based on tables and textual inputs. TABT5 overcomes the encoder-only limitation by incorporating a decoder component and leverages the input structure with table specific embeddings and pre-training. TABT5 achieves new state-of-the-art results on several domains, including spreadsheet formula prediction with a 15% increase in sequence accuracy, QA with a 2.5% increase in sequence accuracy and data-to-text generation with a 2.5% increase in BLEU.

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Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun. 2022-10-17. Table-To-Text generation and pre-training with TabT5. https://arxiv.org/abs/2210.09162

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