Given the task definition and input, reply with output. In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

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.
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?