

Overview
The TAPEX model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu,
Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. TAPEX pre-trains a BART model to solve synthetic SQL queries, after
which it can be fine-tuned to answer natural language questions related to tabular data, as well as performing table fact checking. 
TAPEX has been fine-tuned on several datasets: 
- SQA (Sequential Question Answering by Microsoft)
- WTQ (Wiki Table Questions by Stanford University)
- WikiSQL (by Salesforce)
- TabFact (by USCB NLP Lab).
The abstract from the paper is the following:
Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is
still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data. In this paper, we
propose TAPEX to show that table pre-training can be achieved by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically
synthesizing executable SQL queries and their execution outputs. TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL
executor on the diverse, large-scale and high-quality synthetic corpus. We evaluate TAPEX on four benchmark datasets. Experimental results demonstrate that
TAPEX outperforms previous table pre-training approaches by a large margin and achieves new state-of-the-art results on all of them. This includes improvements
on the weakly-supervised WikiSQL denotation accuracy to 89.5% (+2.3%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy
to 74.5% (+3.5%), and the TabFact accuracy to 84.2% (+3.2%). To our knowledge, this is the first work to exploit table pre-training via synthetic executable programs
and to achieve new state-of-the-art results on various downstream tasks.
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