TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

• URL: http://arxiv.org/abs/2005.08314v1
• Date: Sun, 17 May 2020 17:26:40 GMT
• Title: TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
• Authors: Pengcheng Yin, Graham Neubig, Wen-tau Yih, Sebastian Riedel
• Abstract summary: We present TaBERT, a pretrained LM that jointly learns representations for NL sentences and tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. Implementation of the model will be available at http://fburl.com/TaBERT.
• Score: 113.29476656550342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider. Implementation of the model will be available at http://fburl.com/TaBERT .

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