We attribute the improvement to two potential advantages of our AdaQA model: (i) for the two previous baseline methods, the interaction between question and answer takes place either before or after convolution.
in our AdaQA model, the communication between two sentences is inherent in the convolution operation, and thus can provide the abstracted features with more flexibility; (ii) the bidirectional filter generation mechanism in our AdaQA model generates co-dependent representations for the question and candidate answer, which could enable the model to recover from initial local maxima corresponding to incorrect predictions (Xiong et al., 2017).