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A large amount of information is stored in data tables. Users can search fordata tables using a keyword-based query. A table is composed primarily of datavalues that are organized in rows and columns providing implicit structuralinformation. A table is usually accompanied by secondary information such asthe caption, page title, etc., that form the textual information. Understandingthe connection between the textual and structural information is an importantyet neglected aspect in table retrieval as previous methods treat each sourceof information independently. In addition, users can search for data tablesthat are similar to an existing table, and this setting can be seen as acontent-based table retrieval. In this paper, we propose StruBERT, astructure-aware BERT model that fuses the textual and structural information ofa data table to produce context-aware representations for both textual andtabular content of a data table. StruBERT features are integrated in a newend-to-end neural ranking model to solve three table-related downstream tasks:keyword- and content-based table retrieval, and table similarity. We evaluateour approach using three datasets, and we demonstrate substantial improvementsin terms of retrieval and classification metrics over state-of-the-art methods.

StruBERT: Structure-aware BERT for Table Search and Matching