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.

In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier.
Question: What are the components of the multilingual framework?
text-transformations to the messages vector space model Support Vector Machine

The corpus is freely available at the following link
Question: Is this dataset publicly available?
Yes

Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i
brace $ , we select the most relevant tuples from $T$ and $S$ as follows. Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ .
Question: Is an entity linking process used?
No