Semantic concept scorer based on an ensemble of language translation models for question answer system

A system, comprising:

• a memory having instructions stored therein;
• and
• at least one processor in communication with the memory, wherein the at least one processor is configured to execute the instructions to:
• receive an input question in a source language;
• generate a first similarity score based at least in part on a source-language operand, wherein generating the first similarity score includes:
• generating a first constituent similarity score based at least in part on the input question and a corresponding source-language candidate-answers set,
• generating a second constituent similarity score based at least in part on the source-language candidate-answers set and a corresponding source-language evidence set, and
• generating a third constituent similarity score based at least in part on the source-language candidate-answers set and a corresponding source-language concepts-triples set;
• generate a target-language operand based at least in part on the source-language operand;
• generate a second similarity score based at least in part on the target-language operand;
• generate a semantic concept score based at least in part on the first similarity score and the second similarity score;
• generate a set of ranked answers to the input question (“ranked answer set”) based at least in part on the semantic concept score;
• and
• output the ranked answer set.

A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one processor to cause the at least one processor to:

• receive an input question in a source language;
• generate a first similarity score based at least in part on a source-language operand, wherein generating the first similarity score includes:
• generating a first constituent similarity score based at least in part on the input question and a corresponding source-language candidate-answers set,
• generating a second constituent similarity score based at least in part on the source-language candidate-answers set and a corresponding source-language evidence set, and
• generating a third constituent similarity score based at least in part on the source-language candidate-answers set and a corresponding source-language concepts-triples set;
• generate a target-language operand based at least in part on the source-language operand;
• generate a second similarity score based at least in part on the target-language operand;
• generate a semantic concept score based at least in part on the first similarity score and the second similarity score;
• generate a set of ranked answers to the input question (“ranked answer set”) based at least in part on the semantic concept score;
• and
• output the ranked answer set.