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]: Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth. We also generate two maps that delineate the gender distribution in the dataset. Our dataset provides mappings between location, profile information, and language use, which we can leverage to generate maps that reflect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset.
[EX A]: Which demographic dimensions of people do they obtain?

[EX Q]: We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
[EX A]: Which vision-based approaches does this approach outperform?

[EX Q]: In our experiments, the Memory Neural Networks (MemNNs) proposed in babidataset serve as the baselines.
[EX A]:
What word level and character level model baselines are used?