“Table-To-Text Generation and Pre-Training With TabT5”, Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun2022-10-17 (T5 Transformer, tabular ML)⁠:

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.