The first convolution produces a fixed-size character feature vector named mgram features by extracting local features around each character window of the given word and using a max pooling over vertical character windows.
we modify Bi-GRNN of (Chung et al., 2014) into Bi-CGRNN to take word embeddings in order to produce a sentence-wide representation from sentence compositions.