1. A computer program product, comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by a processor to perform an operation comprising: receiving, by an interface of a question answering (QA) system, a question not specifying a value for a first element of input data, of a plurality of elements of input data specified in the question; determining, based on feature classification data generated during a training phase of the QA system, that one possible value of the set of possible values for the first element of input data, if specified in the question, allows the question answering system to increase a confidence score of at least one candidate answer generated for the question without the value for the first element of input data, beyond a specified confidence threshold; identifying a set of possible values for the first element of input data, wherein the set of possible values is a finite set of possible values including a first possible value and a second possible value; generating, by the question answering system, a respective set of candidate answers for the question: (i) without using any of the possible values for the first element of input data, (ii) using the first possible value for the first element of input data, (iii) using the second possible value for the first element of input data, and (iv) using each remaining possible value in the set of possible values for the first element of input data; and returning at least one candidate answer from at least one of the sets of candidate answers as responsive to the question.
1. A computer program product, comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by a processor to perform an operation comprising: receiving, by an interface of a question answering (QA) system, a question not specifying a value for a first element of input data, of a plurality of elements of input data specified in the question; determining, based on feature classification data generated during a training phase of the QA system, that one possible value of the set of possible values for the first element of input data, if specified in the question, allows the question answering system to increase a confidence score of at least one candidate answer generated for the question without the value for the first element of input data, beyond a specified confidence threshold; identifying a set of possible values for the first element of input data, wherein the set of possible values is a finite set of possible values including a first possible value and a second possible value; generating, by the question answering system, a respective set of candidate answers for the question: (i) without using any of the possible values for the first element of input data, (ii) using the first possible value for the first element of input data, (iii) using the second possible value for the first element of input data, and (iv) using each remaining possible value in the set of possible values for the first element of input data; and returning at least one candidate answer from at least one of the sets of candidate answers as responsive to the question. 11. The computer program product of claim 1 , wherein the question answering system generates the feature classification data during the training phase by processing a plurality of other questions prior to receiving the question from the interface, wherein the training phase generates a machine learning model specifying the feature classification data.