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.
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Question: Contexts are either ground-truth contexts from that dataset, or they are Wikipedia passages retrieved using TF-IDF BIBREF24 based on a HotpotQA question. A new non-overlapping set of contexts was again constructed from Wikipedia via HotpotQA using the same method as Round 1. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. 
 Question: What data sources do they use for creating their dataset?

Answer: Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus) RTE5


Question: For English dataset, we crawl 687 cybersecurity articles from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018.  
 Question: Where are the cybersecurity articles used in the model sourced from?

Answer:  from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018


Question: The model has around 836M parameters, of which only 66K are byte embeddings. 
 Question: How many parameters does the model have?

Answer:
model has around 836M parameters