The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated. 
 Question: What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?
word embeddings to generate a new feature, i.e., summarizing a local context