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:
The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).

Ex Output:
What was the textual source to which OpenIE was applied?

Ex Input:
We focus primarily on the smaller data set of 645 hand-labeled articles provided to task participants, both for training and for validation. We pre-trained BERT-base on the 600,000 articles without labels by using the same Cloze task BIBREF5 that BERT had originally used for pre-training.

Ex Output:
How long is the dataset?

Ex Input:
A quantitative measure is required to reliably evaluate the achieved improvement. One of the methods proposed to measure the interpretability is the word intrusion test BIBREF41 . But, this method is expensive to apply since it requires evaluations from multiple human evaluators for each embedding dimension. In this study, we use a semantic category-based approach based on the method and category dataset (SEMCAT) introduced in BIBREF27 to quantify interpretability. Specifically, we apply a modified version of the approach presented in BIBREF40 in order to consider possible sub-groupings within the categories in SEMCAT.

Ex Output:
What experiments do they use to quantify the extent of interpretability?