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:
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.  
 Question: How big is QA-CTS task dataset?

Ex Output:
17,833 sentences, 826,987 characters and 2,714 question-answer pairs


Ex Input:
Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below. 
 Question: Why did they choose WER as evaluation metric?

Ex Output:
WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses


Ex Input:
We evaluate a multi-task approach and three algorithms that explicitly model the task dependencies. 
 Question: Will these findings be robust through different datasets and different question answering algorithms?

Ex Output:

Yes