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]: BIBREF0 (BIBREF0) developed Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs.
[EX A]: What is the Moral Choice Machine?

[EX Q]: We used the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some. The dataset consists of 1,362 unique utterances with a noun phrase containing some (some-NP). For each example with a some-NP, degen2015investigating collected inference strength ratings from at least 10 participants recruited on Amazon's Mechanical Turk. Participants saw both the target utterance and ten utterances from the preceding discourse context. They then rated the similarity between the original utterance like (UNKREF8) and an utterance in which some was replaced with some, but not all like (UNKREF9), on a 7-point Likert scale with endpoints labeled “very different meaning” (1) and “same meaning” (7). Low similarity ratings thus indicate low inference strength, and high similarity ratings indicate high inference strength.
[EX A]: Which dataset do they use?

[EX Q]: Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling).
[EX A]:
What is the exact performance on SQUAD?