TaBERT

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• BERT
• Natural Language Processing (NLP)
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data | P. Yin, G. Neubig, W. Yih, and S. Riedel
• facebookresearch/TaBERT | GitHub

TaBERT is a model that has been pretrained to learn representations for both [[Natural Language Processing (NLP) | natural language] sentences and tabular data. These sorts of representations are useful for natural language understanding tasks that involve joint reasoning over natural language sentences and tables. ...This is a pretraining approach across structured and unstructured domains, and it opens new possibilities regarding semantic parsing, where one of the key challenges has been understanding the structure of a DB table and how it aligns with a query. TaBERT has been trained using a corpus of 26 million tables and their associated English sentences. Previous pretrained language models have typically been trained using only free-form natural language text. While these models are useful for tasks that require reasoning only for free-form natural language, they aren’t suitable for tasks like DB-based question answering, which requires reasoning over both free-form language and DB tables.TaBERT: A new model for understanding queries over tabular data | Facebook AI