TAPEX: Table Pre-training via Learning a Neural SQL Executor

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 the 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. Our code can be found at https://github.com/microsoft/Table-Pretraining.

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Results from the Paper

 Ranked #1 on Semantic Parsing on WikiSQL (Denotation accuracy (test) metric)

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Semantic Parsing SQA TAPEX-Large Denotation Accuracy 74.5 # 1
Table-based Fact Verification TabFact TAPEX-Large Test 84.2 # 5
Val 84.6 # 2
Semantic Parsing WikiSQL TAPEX-Large (weak supervision) Denotation accuracy (test) 89.5 # 1
Semantic Parsing WikiTableQuestions TAPEX-Large Accuracy (Dev) 57.0 # 6
Accuracy (Test) 57.5 # 6