instruction:
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.
question:
Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data. 
 Question: Which models/frameworks do they compare to?
answer:
MLP


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


question:
We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of “[rationales$|$concept-set]” (i.e. concatenating the rationale sentences and original concept-set strings). 
 Question: Are the models required to also generate rationales?
answer:

No