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
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Q: VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0 
 Question: From when are many VQA datasets collected?

A: late 2014
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Q: Our approach improves LR by 5.17% (Accuracy) and 18.38% (AUC), and MLP by 10.71% (Accuracy) and 30.27% (AUC) on average. Such significant improvements clearly demonstrate that our approach is effective at improving model performance. 
 Question: How are the accuracy merits of the approach demonstrated?

A:
significant improvements clearly demonstrate that our approach is effective at improving model performance
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