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.

Q: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.

A: What annotations are available in the dataset - tweat used hate speach or not?
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Q: Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).

As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas.

A: Are the word embeddings evaluated?
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Q: We trained word embeddings using either GloVe BIBREF11 or SGNS BIBREF12 on a small or a large corpus.

A:
What types of word representations are they evaluating?
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