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 Q]: We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message).
[EX A]: What baselines did they consider?

[EX Q]: The training objective is to minimize the negative likelihood of the aligned unanswerable question $ ilde{q}$ given the answerable question $q$ and its corresponding paragraph $p$ that contains the answer $a$ : L=-(q,q,p,a)DP(q|q,p,a;) where $\mathcal {D}$ is the training corpus and $ heta $ denotes all the parameters. Sequence-to-sequence and pair-to-sequence models are trained with the same objective.
[EX A]: What is the training objective of their pair-to-sequence model?

[EX Q]: We collect three years of online news articles from June 2016 to June 2019.
[EX A]:
What unlabeled corpus did they use?