Table-To-Text generation and pre-training with TabT5Download PDF


17 Apr 2022, 23:25 (modified: 21 Apr 2022, 23:35)ACL ARR 2022 April Blind SubmissionReaders: Everyone
Abstract: Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS. 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.
Paper Type: short
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