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:
This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class).

Ex Output:
How can the classifier facilitate the annotation task for human annotators?


Ex Input:
The training objective is to minimize the negative likelihood of the aligned unanswerable question $\tilde{q}$ 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 $\theta $ denotes all the parameters. Sequence-to-sequence and pair-to-sequence models are trained with the same objective.

Ex Output:
What is the training objective of their pair-to-sequence model?


Ex Input:
Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data.

Ex Output:

Which models/frameworks do they compare to?