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: We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 .
Example Output: On which dataset is the experiment conducted?

Example Input: The attention function BIBREF11 is used to compute the similarity score between passages and questions as: INLINEFORM2
Example Output: Do they use attention?

Example Input: To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function.
Example Output:
How do they combine text representations with the knowledge graph embeddings?