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 street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication. 
 Question: What data did they use?

SOLUTION:  dataset on Mechanical Turk involving human perception, action and communication

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: We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance.  On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100.  
 Question: How are the two different models trained?

SOLUTION:
They pre-train the models using 600000 articles as an unsupervised dataset and then fine-tune the models on small training set.