Question answering is the task where a document context is provided along with a question whose answer is present within the given document context. Existing models for question answering used to optimize the cross-entropy loss, which used to encourage the exact answers and penalize other probable answers that are equally accurate as the exact answer. These existing question answering models (state of the art dynamic coattention network by Xiong et. al. 2017) are trained to output exact answer spans from the document context for the question asked. The start and end position of the actual ground truth answer is used as the target for this supervised learning approach. Thus, this supervised model uses cross-entropy loss over both the positions and the objective is to minimize this overall loss over both the positions.
As we can see, the optimization is done...
