This is because a token encoded by a bidirectional RNN will incorporate evidence from the entire input sequence, but the CNN's representation is limited by the effective input width 1 of the network: the size of the input context which is observed, directly or indirectly, by the representation of a token at a given layer in the network.
in a network composed of a series of stacked convolutional layers of convolution width w, the number r of context tokens incorporated into a token's representation at a given layer l, is given by r = l(w − 1) + 1.