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]: For training data, 3 million webpages from this corpus were processed with a CCG parser to produce logical forms BIBREF10 . We also used the test set created by Krishnamurthy and Mitchell, which contains 220 queries generated in the same fashion as the training data from a separate section of ClueWeb. However, as they did not release a development set with their data, we used this set as a development set. This final test set contains 307 queries.
[EX A]: How big is their dataset?

[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]: We evaluate the adversarially trained models, as shown in Table TABREF18.

After adversarial training, the performance of all the target models raises significantly, while that on the original examples remain comparable.
[EX A]:
How much in experiments is performance improved for models trained with generated adversarial examples?