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.

We evaluated our models on 3 different datasets:

CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR).

20 newsgroups for topic identification task, consisting of written text;

Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual); 
 Question: What datasets did they use for evaluation?
CSAT dataset 20 newsgroups Fisher Phase 1 corpus

Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts. 
 Question: What data is the language model pretrained on?
Chinese general corpus

Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images. 
 Question: What challenges remain unresolved?

give more independence to the several learning methods (e.g. less human intervention) involved in the studies increasing the size of the output images