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.

Ex Input:
CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities.

Ex Output:
Is new evaluation metric extension of ROGUE?


Ex 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 .

Ex Output:
On which dataset is the experiment conducted?


Ex Input:
Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9.

Ex Output:

What was the baseline?