Method and system for table retrieval using multimodal deep co-learning with helper query-dependent and query-independent relevance labels

A computer implemented method for ranking tabular data entities by likelihood of comprising answers for (natural language) queries, comprising:

• receiving a query and a plurality of descriptions of tabular entities, each description comprising a plurality of separate representations, and each of the plurality of separate representations represents a tabular data aspect;
• generating a joint representation by combining a plurality of combined representations from the plurality of separate representations, wherein the combining is executed by at least one gated multimodal unit;
• and
• generating a ranking for at least one of the tabular entities descriptions by combining the query and the joint representation.

A computer implemented method of training a tabular data entities ranking system, the ranking is by likelihood of comprising answers for (natural language) queries, and the training comprising:

• receiving a query and a plurality of descriptions of tabular entities, each description comprising a plurality of separate representations, and each of the plurality of separate representations represents a tabular data aspect;
• generating a joint representation by combining a plurality of combined representations from the plurality of separate representations, wherein the combining is executed by at least one gated multimodal unit;
• generating a ranking for at least one of the tabular entities descriptions by combining the query and the joint representation;
• combining at least two representations from the plurality of separate representations for calculating of a joint loss;
• and
• training at least one part of the tabular data entities ranking system using a loss function comprising the joint loss.