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.

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

Ex Output:
CSKS task

Ex Input:
Automation of cQA forums can be divided into three tasks: question-comment relevance (Task A), question-question relevance (Task B), and question-external comment relevance (Task C). In our cQA tasks, the pair of objects are (question, question) or (question, comment), and the relationship is relevant/irrelevant. For task C, in addition to an original question (oriQ) and an external comment (relC), the question which relC commented on is also given (relQ). To incorporate this extra information, we consider a multitask learning framework which jointly learns to predict the relationships of the three pairs (oriQ/relQ, oriQ/relC, relQ/relC).
Question: What supplemental tasks are used for multitask learning?

Ex Output:
Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question

Ex Input:
Moreover, to be able to make use of multiple answer styles within a single system, our model introduces an artificial token corresponding to the target style at the beginning of the answer sentence ( $y_1$ ), like BIBREF14 . At test time, the user can specify the first token to control the answer styles.
Question: Does their model also take the expected answer style as input?

Ex Output:
Yes