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]: 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?
[A]: 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

[Q]: As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks.
Question: Do they fine-tune their model on the end task?
[A]: Yes

[Q]: We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space.
Question: Are language-specific and language-neutral components disjunctive?
[A]:
No