In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Input: Consider Input: To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.
Question: What was the baseline model?

Output: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs

Input: Consider Input: We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like "fire", "earthquake", "theft", "robbery", "drunk driving", "drunk driving accident" etc.
Question: Do the tweets come from any individual?

Output: Yes

Input: Consider Input: In tab:blueperpl we report BLEU scores for the reconstruction of test set sentences from their content and feature representations, as well as the model perplexities of the reconstruction. For both models, we use beam decoding with a beam size of eight. Note that for both models, the all and top classification accuracy tends to be quite similar, though for the Baseline they are often almost exactly the same when the Baseline has little to no diversity in the outputs.
Question: What metrics are used for automatic evaluation?
Output: classification accuracy BLEU scores model perplexities of the reconstruction