AVOIDING SUPPORTING EVIDENCE PROCESSING WHEN EVIDENCE SCORING DOES NOT AFFECT FINAL RANKING OF A CANDIDATE ANSWER
First Claim
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1. A method to provide selective supporting evidence, comprising:
- applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer;
determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer; and
upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.
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Abstract
Methods to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.
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7 Claims
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1. A method to provide selective supporting evidence, comprising:
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applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer; determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer; and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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Specification