TASK DEFINITION: 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.
PROBLEM: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. 
 Question: what types of features were used?

SOLUTION: stylometric, lexical, grammatical, and semantic

PROBLEM: 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?

SOLUTION:  extraction-then-synthesis framework

PROBLEM: Cloze Track User Query Track 
 Question: What two types the Chinese reading comprehension dataset consists of?

SOLUTION:
cloze-style reading comprehension and user query reading comprehension questions