Intelligent question answering on tabular content

A method of answering user queries for tabular data, comprising:

• accessing at least one input table using an input user query that pertains to the at least one input table;
• extracting row and column information from the input table;
• storing data from a first row of the at least one input table as corresponding column headers of the at least one input table;
• storing data from a first column as pertaining to a primary entity that forms relationships with the column headers of the at least one input table;
• combining the primary entity with each of the column headers to form row-level sentences;
• combining the column headers with contents of corresponding columns to form column-level sentences;
• combining the row-level sentences and the column-level sentences to form a paragraph of text corresponding to the at least one input table;
• generating representation for the at least one input table, wherein the representation includes generated text, row_ids, column_ids and data features extracted from the at least one table;
• providing the input user query and the representation to a TAQuans model;
• obtaining from the TAQuans model, a candidate answer to the input user query pertaining to the at least one input table;
• and
• generating a response to the input user query wherein the response includes the candidate answer and a tabular portion with highlighted portion of the at least one input table that includes the candidate answer.

A non-transitory processor-readable storage medium comprising machine-readable instructions that cause a processor to:

• train a TAQuAns (table QuAns) model to respond to user queries regarding information in tables;
• access at least one input table using an input user query that pertains to the at least one input table;
• extract row information regarding one or more rows and including one or more column headers from the at least one input table;
• extract column information regarding one or more columns and including one or more row headers from the at least one input table;
• form a paragraph of table text from the row information and the column information extracted from the at least one input table;
• provide the paragraph of table text and the input user query to the TAQuAns model;
• obtain at least a portion of the paragraph of table text as a candidate answer to the input user query from the TAQuAns model based on the input user query;
• and
• provide the candidate answer as a textual portion of a response to the input user query via an output interface.