Patent ID: 9384450
Filing Date: 2016-07-05
CPC Classification: G06N

Claim Text:
1. A method for training a machine learning model for open domain question answering, comprising: receiving one or more first trained classifiers for question answering; using the received one or more first trained classifiers to generate a set of candidate answers to a question; using one or more second trained classifiers for scoring each of the candidate answers, wherein the scoring indicates a measure of how well each candidate answer answers the question, and wherein using the second trained classifiers for scoring each of the candidate answers includes comparing each candidate answer to a first ground truth corresponding to the question; presenting a set of top-scoring candidate answers to a human operator; receiving an indication from the human operator as to whether each presented candidate answer is a correct answer for the question or an incorrect answer for the question; treating the candidate answers indicated as correct by the human operator as an additional ground truth for the question; and using the additional ground truth for the question to further train the first trained classifiers.