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.

Q: We took inspiration from these works to design our experiments to solve the CSKS task. 
 Question: What problem do they apply transfer learning to?

A: CSKS task
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Q: 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?

A: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs
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Q: We model the ASP placement task as a successor of the AEP task. For all the `relevant' news entity pairs, the task is to determine the correct entity section. Each section in a Wikipedia entity page represents a different topic. For example, Barack Obama has the sections `Early Life', `Presidency', `Family and Personal Life' etc. Article-Section Ground-truth. The dataset consists of the triple INLINEFORM0 , where INLINEFORM1 , where we assume that INLINEFORM2 has already been determined as relevant. We therefore have a multi-class classification problem where we need to determine the section of INLINEFORM3 where INLINEFORM4 is cited.  
 Question: How do they determine the exact section to use the input article?

A:
They use a multi-class classifier to determine the section it should be cited
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