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.

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

Example Input: In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet.
Example Output: Are trained word embeddings used for any other NLP task?

Example Input: The data used for this task is drawn from the Switchboard conversational English corpus BIBREF31 .
Example Output:
Which dataset do they use?