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.

Example Input: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific. 
 Question: What does an individual model consist of?
Example Output: Bayesian model of garg2012unsupervised as our base monolingual model

Example Input: Then we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0 We note that the similarity metric defined above for sentences is equally applicable for n-gram retrieval. 
 Question: Which similarity measure do they use in their n-gram retrieval approach?
Example Output: we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0

Example Input: 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. 
 Question: Are the word embeddings evaluated?
Example Output:
Yes