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: Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation.
Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
Example Output: continuous relaxation to top-k-argmax

Example Input: 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?
Example Output: text-transformations to the messages vector space model Support Vector Machine

Example Input: We then ask a separate set of Turkers to rate the stories for overall quality and the three improvement areas. All ratings are on a five-point scale. We collect two ratings per story, and throw out ratings that disagree by more than 2 points. A total of 11% of ratings were thrown out, leaving four metrics across 241 stories for analysis.
Question: How do they evaluate generated stories?
Example Output:
separate set of Turkers to rate the stories for overall quality and the three improvement areas