In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: Finally, we combine our CNN and RNN models using a voting process. For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes. In case of a tie, we pick one of the most frequent classes randomly.

A: How does their simple voting scheme work?
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Q: We compare our model with other strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard.

A: What other solutions do they compare to?
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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. 

A:
How do they determine the exact section to use the input article?
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