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.
@article{arxiv.2210.09162,
title = {Table-To-Text generation and pre-training with TabT5},
author = {Ewa Andrejczuk and Julian Martin Eisenschlos and Francesco Piccinno and Syrine Krichene and Yasemin Altun},
journal= {arXiv preprint arXiv:2210.09162},
year = {2022}
}