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.

Example Input: In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers. 
 Question: Which framework they propose in this paper?
Example Output:  extraction-then-synthesis framework

Example Input: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). 
 Question: what lexical features did they experiment with?
Example Output: TF.IDF-based features

Example Input: As a warm-up procedure, the first 100 posts were annotated by two annotators (the author and the supervisor) and the results compared. The remainder of the annotation task was performed by the author, resulting in 3600 annotated samples. 
 Question: Who were the annotators?
Example Output:
the author and the supervisor